TL;DR: This paper from Open AI introduces GPT-2. In their blog post...
Motivation: build a general system that can perform well on many ta...
GPT-2 is the largest model that they present here, and importantly,...
Limitations of "narrow AI": "Current ML systems need hundreds to t...
I️n a causal language model, the goal is to predict the next word c...
The approach of transfer learning is still widely successful/used i...
"In this paper, we connect these two lines of work and continue the...
Part of the uniqueness of their approach, is that they can perform ...
There were only 10MB of naturally occurring demonstrations of Engli...
Hypothesis: "Our speculation is that a language model with suffici...
Besides scaling up GPT by an order of magnitude, the main innovatio...
![Imgur](https://imgur.com/p6NirSw.png)
Here is a great tutorial on GPT-2: https://jalammar.github.io/illu...
A great explanation of BPE can be found here: https://huggingface.c...
For a general AI system, this byte-level approach has clear advanta...
Summary of their zero-shot learning results.
Interesting that they can have decent performance on translation wi...
Good curve-> clearly there are more gains to be had on WebText as t...
Language Models are Unsupervised Multitask Learners
Alec Radford
* 1
Jeffrey Wu
* 1
Rewon Child
1
David Luan
1
Dario Amodei
** 1
Ilya Sutskever
** 1
Abstract
Natural language processing tasks, such as ques-
tion answering, machine translation, reading com-
prehension, and summarization, are typically
approached with supervised learning on task-
specific datasets. We demonstrate that language
models begin to learn these tasks without any ex-
plicit supervision when trained on a new dataset
of millions of webpages called WebText. When
conditioned on a document plus questions, the an-
swers generated by the language model reach 55
F1 on the CoQA dataset - matching or exceeding
the performance of 3 out of 4 baseline systems
without using the 127,000+ training examples.
The capacity of the language model is essential
to the success of zero-shot task transfer and in-
creasing it improves performance in a log-linear
fashion across tasks. Our largest model, GPT-2,
is a 1.5B parameter Transformer that achieves
state of the art results on 7 out of 8 tested lan-
guage modeling datasets in a zero-shot setting
but still underfits WebText. Samples from the
model reflect these improvements and contain co-
herent paragraphs of text. These findings suggest
a promising path towards building language pro-
cessing systems which learn to perform tasks from
their naturally occurring demonstrations.
1. Introduction
Machine learning systems now excel (in expectation) at
tasks they are trained for by using a combination of large
datasets, high-capacity models, and supervised learning
(Krizhevsky et al., 2012) (Sutskever et al., 2014) (Amodei
et al., 2016). Yet these systems are brittle and sensitive to
slight changes in the data distribution (Recht et al., 2018)
and task specification (Kirkpatrick et al., 2017). Current sys-
tems are better characterized as narrow experts rather than
*, **
Equal contribution
1
OpenAI, San Francisco, Califor-
nia, United States. Correspondence to: Alec Radford
<alec@openai.com>.
competent generalists. We would like to move towards more
general systems which can perform many tasks – eventually
without the need to manually create and label a training
dataset for each one.
The dominant approach to creating ML systems is to col-
lect a dataset of training examples demonstrating correct
behavior for a desired task, train a system to imitate these
behaviors, and then test its performance on independent
and identically distributed (IID) held-out examples. This
has served well to make progress on narrow experts. But
the often erratic behavior of captioning models (Lake et al.,
2017), reading comprehension systems (Jia & Liang, 2017),
and image classifiers (Alcorn et al., 2018) on the diversity
and variety of possible inputs highlights some of the short-
comings of this approach.
Our suspicion is that the prevalence of single task training
on single domain datasets is a major contributor to the lack
of generalization observed in current systems. Progress
towards robust systems with current architectures is likely
to require training and measuring performance on a wide
range of domains and tasks. Recently, several benchmarks
have been proposed such as GLUE (Wang et al., 2018) and
decaNLP (McCann et al., 2018) to begin studying this.
Multitask learning (Caruana, 1997) is a promising frame-
work for improving general performance. However, mul-
titask training in NLP is still nascent. Recent work re-
ports modest performance improvements (Yogatama et al.,
2019) and the two most ambitious efforts to date have
trained on a total of 10 and 17
(dataset, objective)
pairs respectively (McCann et al., 2018) (Bowman et al.,
2018). From a meta-learning perspective, each
(dataset,
objective)
pair is a single training example sampled
from the distribution of datasets and objectives. Current
ML systems need hundreds to thousands of examples to
induce functions which generalize well. This suggests that
multitask training many need just as many effective training
pairs to realize its promise with current approaches. It will
be very difficult to continue to scale the creation of datasets
and the design of objectives to the degree that may be re-
quired to brute force our way there with current techniques.
This motivates exploring additional setups for performing
multitask learning.
The current best performing systems on language tasks
Language Models are Unsupervised Multitask Learners
Figure 1.
Zero-shot task performance of WebText LMs as a function of model size on many NLP tasks. Reading Comprehension results
are on CoQA (Reddy et al., 2018), translation on WMT-14 Fr-En (Artetxe et al., 2017), summarization on CNN and Daily Mail (See et al.,
2017), and Question Answering on Natural Questions (Kwiatkowski et al., 2019). Section 3 contains detailed descriptions of each result.
utilize a combination of pre-training and supervised fine-
tuning. This approach has a long history with a trend to-
wards more flexible forms of transfer. First, word vectors
were learned and used as inputs to task-specific architec-
tures (Mikolov et al., 2013) (Collobert et al., 2011), then
the contextual representations of recurrent networks were
transferred (Dai & Le, 2015) (Peters et al., 2018), and re-
cent work suggests that task-specific architectures are no
longer necessary and transferring many self-attention blocks
is sufficient (Radford et al., 2018) (Devlin et al., 2018).
These methods still require supervised training in order
to perform a task. When only minimal or no supervised
data is available, another line of work has demonstrated
the promise of language models to perform specific tasks,
such as commonsense reasoning (Schwartz et al., 2017) and
sentiment analysis (Radford et al., 2017).
In this paper, we connect these two lines of work and con-
tinue the trend of more general methods of transfer. We
demonstrate language models can perform down-stream
tasks in a zero-shot setting – without any parameter or archi-
tecture modification. We demonstrate this approach shows
potential by highlighting the ability of language models to
perform a wide range of tasks in a zero-shot setting. We
achieve promising, competitive, and state of the art results
depending on the task.
2. Approach
At the core of our approach is language modeling. Lan-
guage modeling is usually framed as unsupervised distri-
bution estimation from a set of examples
(x
1
, x
2
, ..., x
n
)
each composed of variable length sequences of symbols
(s
1
, s
2
, ..., s
n
)
. Since language has a natural sequential or-
dering, it is common to factorize the joint probabilities over
symbols as the product of conditional probabilities (Jelinek
& Mercer, 1980) (Bengio et al., 2003):
p(x) =
n
Y
i=1
p(s
n
|s
1
, ..., s
n1
) (1)
This approach allows for tractable sampling from and es-
timation of
p(x)
as well as any conditionals of the form
p(s
nk
, ..., s
n
|s
1
, ..., s
nk1
)
. In recent years, there have
been significant improvements in the expressiveness of mod-
els that can compute these conditional probabilities, such as
self-attention architectures like the Transformer (Vaswani
et al., 2017).
Learning to perform a single task can be expressed in a
probabilistic framework as estimating a conditional distri-
bution
p(output|input)
. Since a general system should be
able to perform many different tasks, even for the same
input, it should condition not only on the input but also
on the task to be performed. That is, it should model
p(output|input, task)
. This has been variously formalized
in multitask and meta-learning settings. Task conditioning
is often implemented at an architectural level, such as the
task specific encoders and decoders in (Kaiser et al., 2017)
or at an algorithmic level such as the inner and outer loop
optimization framework of MAML (Finn et al., 2017). But
as exemplified in McCann et al. (2018), language provides
a flexible way to specify tasks, inputs, and outputs all as a
sequence of symbols. For example, a translation training
example can be written as the sequence
(translate to
french, english text, french text)
. Like-
wise, a reading comprehension training example can
be written as
(answer the question, document,
question, answer)
. McCann et al. (2018) demon-
strated it was possible to train a single model, the MQAN,
Language Models are Unsupervised Multitask Learners
to infer and perform many different tasks on examples with
this type of format.
Language modeling is also able to, in principle, learn the
tasks of McCann et al. (2018) without the need for explicit
supervision of which symbols are the outputs to be pre-
dicted. Since the supervised objective is the the same as the
unsupervised objective but only evaluated on a subset of the
sequence, the global minimum of the unsupervised objective
is also the global minimum of the supervised objective. In
this slightly toy setting, the concerns with density estimation
as a principled training objective discussed in (Sutskever
et al., 2015) are side stepped. The problem instead becomes
whether we are able to, in practice, optimize the unsuper-
vised objective to convergence. Preliminary experiments
confirmed that sufficiently large language models are able to
perform multitask learning in this toy-ish setup but learning
is much slower than in explicitly supervised approaches.
While it is a large step from the well-posed setup described
above to the messiness of “language in the wild”, Weston
(2016) argues, in the context of dialog, for the need to
develop systems capable of learning from natural language
directly and demonstrated a proof of concept – learning a
QA task without a reward signal by using forward prediction
of a teacher’s outputs. While dialog is an attractive approach,
we worry it is overly restrictive. The internet contains a vast
amount of information that is passively available without
the need for interactive communication. Our speculation is
that a language model with sufficient capacity will begin
to learn to infer and perform the tasks demonstrated in
natural language sequences in order to better predict them,
regardless of their method of procurement. If a language
model is able to do this it will be, in effect, performing
unsupervised multitask learning. We test whether this is the
case by analyzing the performance of language models in a
zero-shot setting on a wide variety of tasks.
2.1. Training Dataset
Most prior work trained language models on a single do-
main of text, such as news articles (Jozefowicz et al., 2016),
Wikipedia (Merity et al., 2016), or fiction books (Kiros
et al., 2015). Our approach motivates building as large and
diverse a dataset as possible in order to collect natural lan-
guage demonstrations of tasks in as varied of domains and
contexts as possible.
A promising source of diverse and nearly unlimited text is
web scrapes such as Common Crawl. While these archives
are many orders of magnitude larger than current language
modeling datasets, they have significant data quality issues.
Trinh & Le (2018) used Common Crawl in their work on
commonsense reasoning but noted a large amount of doc-
uments “whose content are mostly unintelligible”. We ob-
served similar data issues in our initial experiments with
”I’m not the cleverest man in the world, but like they say in
French: Je ne suis pas un imbecile [I’m not a fool].
In a now-deleted post from Aug. 16, Soheil Eid, Tory candidate
in the riding of Joliette, wrote in French:
Mentez mentez,
il en restera toujours quelque chose
, which translates as,
Lie lie and something will always remain.
“I hate the word ‘
perfume
,”’ Burr says. ‘It’s somewhat better
in French: parfum.
If listened carefully at 29:55, a conversation can be heard
between two guys in French:
-Comment on fait pour aller
de l’autre cot
´
e? -Quel autre cot
´
e?
”, which means
- How
do you get to the other side? - What side?”.
If this sounds like a bit of a stretch, consider this ques-
tion in French:
As-tu aller au cin
´
ema?
, or
Did you go to
the movies?
, which literally translates as Have-you to go to
movies/theater?
Brevet Sans Garantie Du Gouvernement
”, translated to
English: Patented without government warranty”.
Table 1.
Examples of naturally occurring demonstrations of En-
glish to French and French to English translation found throughout
the WebText training set.
Common Crawl. Trinh & Le (2018)’s best results were
achieved using a small subsample of Common Crawl which
included only documents most similar to their target dataset,
the Winograd Schema Challenge. While this is a pragmatic
approach to improve performance on a specific task, we
want to avoid making assumptions about the tasks to be
performed ahead of time.
Instead, we created a new web scrape which emphasizes
document quality. To do this we only scraped web pages
which have been curated/filtered by humans. Manually
filtering a full web scrape would be exceptionally expensive
so as a starting point, we scraped all outbound links from
Reddit, a social media platform, which received at least 3
karma. This can be thought of as a heuristic indicator for
whether other users found the link interesting, educational,
or just funny.
The resulting dataset, WebText, contains the text subset
of these 45 million links. To extract the text from HTML
responses we use a combination of the Dragnet (Peters &
Lecocq, 2013) and Newspaper
1
content extractors. All re-
sults presented in this paper use a preliminary version of
WebText which does not include links created after Dec
2017 and which after de-duplication and some heuristic
based cleaning contains slightly over 8 million documents
for a total of 40 GB of text. We removed all Wikipedia
documents from WebText since it is a common data source
for other datasets and could complicate analysis due to over-
1
https://github.com/codelucas/newspaper
Language Models are Unsupervised Multitask Learners
lapping training data with test evaluation tasks.
2.2. Input Representation
A general language model (LM) should be able to compute
the probability of (and also generate) any string. Current
large scale LMs include pre-processing steps such as lower-
casing, tokenization, and out-of-vocabulary tokens which
restrict the space of model-able strings. While processing
Unicode strings as a sequence of UTF-8 bytes elegantly ful-
fills this requirement as exemplified in work such as Gillick
et al. (2015), current byte-level LMs are not competitive
with word-level LMs on large scale datasets such as the
One Billion Word Benchmark (Al-Rfou et al., 2018). We
observed a similar performance gap in our own attempts to
train standard byte-level LMs on WebText.
Byte Pair Encoding (BPE) (Sennrich et al., 2015) is a
practical middle ground between character and word level
language modeling which effectively interpolates between
word level inputs for frequent symbol sequences and char-
acter level inputs for infrequent symbol sequences. Despite
its name, reference BPE implementations often operate on
Unicode code points and not byte sequences. These imple-
mentations would require including the full space of Uni-
code symbols in order to model all Unicode strings. This
would result in a base vocabulary of over 130,000 before
any multi-symbol tokens are added. This is prohibitively
large compared to the 32,000 to 64,000 token vocabularies
often used with BPE. In contrast, a byte-level version of
BPE only requires a base vocabulary of size 256. However,
directly applying BPE to the byte sequence results in sub-
optimal merges due to BPE using a greedy frequency based
heuristic for building the token vocabulary. We observed
BPE including many versions of common words like
dog
since they occur in many variations such as
dog. dog!
dog?
. This results in a sub-optimal allocation of limited
vocabulary slots and model capacity. To avoid this, we pre-
vent BPE from merging across character categories for any
byte sequence. We add an exception for spaces which sig-
nificantly improves the compression efficiency while adding
only minimal fragmentation of words across multiple vocab
tokens.
This input representation allows us to combine the empirical
benefits of word-level LMs with the generality of byte-level
approaches. Since our approach can assign a probability to
any Unicode string, this allows us to evaluate our LMs on
any dataset regardless of pre-processing, tokenization, or
vocab size.
2.3. Model
We use a Transformer (Vaswani et al., 2017) based archi-
tecture for our LMs. The model largely follows the details
of the OpenAI GPT model (Radford et al., 2018) with a
Parameters Layers d
model
117M 12 768
345M 24 1024
762M 36 1280
1542M 48 1600
Table 2. Architecture hyperparameters for the 4 model sizes.
few modifications. Layer normalization (Ba et al., 2016)
was moved to the input of each sub-block, similar to a
pre-activation residual network (He et al., 2016) and an
additional layer normalization was added after the final self-
attention block. A modified initialization which accounts
for the accumulation on the residual path with model depth
is used. We scale the weights of residual layers at initial-
ization by a factor of
1/
N
where
N
is the number of
residual layers. The vocabulary is expanded to 50,257. We
also increase the context size from 512 to 1024 tokens and
a larger batchsize of 512 is used.
3. Experiments
We trained and benchmarked four LMs with approximately
log-uniformly spaced sizes. The architectures are summa-
rized in Table 2. The smallest model is equivalent to the
original GPT, and the second smallest equivalent to the
largest model from BERT (Devlin et al., 2018). Our largest
model, which we call GPT-2, has over an order of magni-
tude more parameters than GPT. The learning rate of each
model was manually tuned for the best perplexity on a 5%
held-out sample of WebText. All models still underfit Web-
Text and held-out perplexity has as of yet improved given
more training time.
3.1. Language Modeling
As an initial step towards zero-shot task transfer, we are
interested in understanding how WebText LM’s perform
at zero-shot domain transfer on the primary task they are
trained for – language modeling. Since our model operates
on a byte level and does not require lossy pre-processing
or tokenization, we can evaluate it on any language model
benchmark. Results on language modeling datasets are
commonly reported in a quantity which is a scaled or ex-
ponentiated version of the average negative log probability
per canonical prediction unit - usually a character, a byte, or
a word. We evaluate the same quantity by computing the
log-probability of a dataset according to a WebText LM and
dividing by the number of canonical units. For many of these
datasets, WebText LMs would be tested significantly out-
of-distribution, having to predict aggressively standardized
text, tokenization artifacts such as disconnected punctuation
and contractions, shuffled sentences, and even the string
Language Models are Unsupervised Multitask Learners
LAMBADA LAMBADA CBT-CN CBT-NE WikiText2 PTB enwik8 text8 WikiText103 1BW
(PPL) (ACC) (ACC) (ACC) (PPL) (PPL) (BPB) (BPC) (PPL) (PPL)
SOTA 99.8 59.23 85.7 82.3 39.14 46.54 0.99 1.08 18.3 21.8
117M 35.13 45.99 87.65 83.4 29.41 65.85 1.16 1.17 37.50 75.20
345M 15.60 55.48 92.35 87.1 22.76 47.33 1.01 1.06 26.37 55.72
762M 10.87 60.12 93.45 88.0 19.93 40.31 0.97 1.02 22.05 44.575
1542M 8.63 63.24 93.30 89.05 18.34 35.76 0.93 0.98 17.48 42.16
Table 3.
Zero-shot results on many datasets. No training or fine-tuning was performed for any of these results. PTB and WikiText-2
results are from (Gong et al., 2018). CBT results are from (Bajgar et al., 2016). LAMBADA accuracy result is from (Hoang et al., 2018)
and LAMBADA perplexity result is from (Grave et al., 2016). Other results are from (Dai et al., 2019).
<UNK>
which is extremely rare in WebText - occurring
only 26 times in 40 billion bytes. We report our main re-
sults in Table 3 using invertible de-tokenizers which remove
as many of these tokenization / pre-processing artifacts as
possible. Since these de-tokenizers are invertible, we can
still calculate the log probability of a dataset and they can
be thought of as a simple form of domain adaptation. We
observe gains of 2.5 to 5 perplexity for GPT-2 with these
de-tokenizers.
WebText LMs transfer well across domains and datasets,
improving the state of the art on 7 out of the 8 datasets in a
zero-shot setting. Large improvements are noticed on small
datasets such as Penn Treebank and WikiText-2 which have
only 1 to 2 million training tokens. Large improvements
are also noticed on datasets created to measure long-term
dependencies like LAMBADA (Paperno et al., 2016) and
the Children’s Book Test (Hill et al., 2015). Our model is
still significantly worse than prior work on the One Billion
Word Benchmark (Chelba et al., 2013). This is likely due
to a combination of it being both the largest dataset and
having some of the most destructive pre-processing - 1BW’s
sentence level shuffling removes all long-range structure.
3.2. Children’s Book Test
Figure 2.
Performance on the Children’s Book Test as a function of
model capacity. Human performance are from Bajgar et al. (2016),
instead of the much lower estimates from the original paper.
The Children’s Book Test (CBT) (Hill et al., 2015) was
created to examine the performance of LMs on different cat-
egories of words: named entities, nouns, verbs, and preposi-
tions. Rather than reporting perplexity as an evaluation met-
ric, CBT reports accuracy on an automatically constructed
cloze test where the task is to predict which of 10 possible
choices for an omitted word is correct. Following the LM
approach introduced in the original paper, we compute the
probability of each choice and the rest of the sentence con-
ditioned on this choice according to the LM, and predict
the one with the highest probability. As seen in Figure 2
performance steadily improves as model size is increased
and closes the majority of the gap to human performance
on this test. Data overlap analysis showed one of the CBT
test set books, The Jungle Book by Rudyard Kipling, is in
WebText, so we report results on the validation set which
has no significant overlap. GPT-2 achieves new state of the
art results of 93.3% on common nouns and 89.1% on named
entities. A de-tokenizer was applied to remove PTB style
tokenization artifacts from CBT.
3.3. LAMBADA
The LAMBADA dataset (Paperno et al., 2016) tests the
ability of systems to model long-range dependencies in
text. The task is to predict the final word of sentences
which require at least 50 tokens of context for a human to
successfully predict. GPT-2 improves the state of the art
from 99.8 (Grave et al., 2016) to 8.6 perplexity and increases
the accuracy of LMs on this test from 19% (Dehghani et al.,
2018) to 52.66%. Investigating GPT-2’s errors showed most
predictions are valid continuations of the sentence, but are
not valid final words. This suggests that the LM is not
using the additional useful constraint that the word must be
the final of the sentence. Adding a stop-word filter as an
approximation to this further increases accuracy to 63.24%,
improving the overall state of the art on this task by 4%. The
previous state of the art (Hoang et al., 2018) used a different
restricted prediction setting where the outputs of the model
were constrained to only words that appeared in the context.
For GPT-2, this restriction is harmful rather than helpful
Language Models are Unsupervised Multitask Learners
since 19% of answers are not in context. We use a version
of the dataset without preprocessing.
3.4. Winograd Schema Challenge
Figure 3.
Performance on the Winograd Schema Challenge as a
function of model capacity.
The Winograd Schema challenge (Levesque et al., 2012)
was constructed to measure the capability of a system to
perform commonsense reasoning by measuring its ability
to resolve ambiguities in text. Recently Trinh & Le (2018)
demonstrated significant progress on this challenge using
LMs, by predicting the resolution of the ambiguity with
higher probability. We follow their problem formulation and
visualize the performance of our models with both full and
partial scoring techniques in Figure 3. GPT-2 improves state
of the art accuracy by 7%, achieving 70.70%. The dataset
is quite small with only 273 examples so we recommend
reading Trichelair et al. (2018) to help contextualize this
result.
3.5. Reading Comprehension
The Conversation Question Answering dataset (CoQA)
Reddy et al. (2018) consists of documents from 7 different
domains paired with natural language dialogues between a
question asker and a question answerer about the document.
CoQA tests reading comprehension capabilities and also
the ability of models to answer questions that depend on
conversation history (such as “Why?”).
Greedy decoding from GPT-2 when conditioned on a doc-
ument, the history of the associated conversation, and a
final token
A:
achieves 55 F1 on the development set. This
matches or exceeds the performance of 3 out of 4 base-
line systems without using the 127,000+ manually collected
question answer pairs those baselines were trained on. The
supervised SOTA, a BERT based system (Devlin et al.,
R-1 R-2 R-L R-AVG
Bottom-Up Sum 41.22 18.68 38.34 32.75
Lede-3 40.38 17.66 36.62 31.55
Seq2Seq + Attn 31.33 11.81 28.83 23.99
GPT-2 TL;DR: 29.34 8.27 26.58 21.40
Random-3 28.78 8.63 25.52 20.98
GPT-2 no hint 21.58 4.03 19.47 15.03
Table 4. Summarization performance as measured by ROUGE F1
metrics on the CNN and Daily Mail dataset. Bottom-Up Sum is
the SOTA model from (Gehrmann et al., 2018)
2018), is nearing the 89 F1 performance of humans. While
GPT-2’s performance is exciting for a system without any su-
pervised training, some inspection of its answers and errors
suggests GPT-2 often uses simple retrieval based heuristics
such as answer with a name from the document in response
to a who question.
3.6. Summarization
We test GPT-2’s ability to perform summarization on the
CNN and Daily Mail dataset (Nallapati et al., 2016). To in-
duce summarization behavior we add the text
TL;DR:
after
the article and generate 100 tokens with Top-
k
random sam-
pling (Fan et al., 2018) with
k = 2
which reduces repetition
and encourages more abstractive summaries than greedy de-
coding. We use the first 3 generated sentences in these 100
tokens as the summary. While qualitatively the generations
resemble summaries, as shown in Table 14, they often focus
on recent content from the article or confuse specific details
such as how many cars were involved in a crash or whether
a logo was on a hat or shirt. On the commonly reported
ROUGE 1,2,L metrics the generated summaries only begin
to approach the performance of classic neural baselines and
just barely outperforms selecting 3 random sentences from
the article. GPT-2’s performance drops by 6.4 points on
the aggregate metric when the task hint is removed which
demonstrates the ability to invoke task specific behavior in
a language model with natural language.
3.7. Translation
We test whether GPT-2 has begun to learn how to translate
from one language to another. In order to help it infer that
this is the desired task, we condition the language model
on a context of example pairs of the format
english
sentence = french sentence
and then after a fi-
nal prompt of
english sentence =
we sample from
the model with greedy decoding and use the first generated
sentence as the translation. On the WMT-14 English-French
test set, GPT-2 gets 5 BLEU, which is slightly worse than
a word-by-word substitution with a bilingual lexicon in-
ferred in previous work on unsupervised word translation
Language Models are Unsupervised Multitask Learners
Question Generated Answer Correct Probability
Who wrote the book the origin of species? Charles Darwin 3 83.4%
Who is the founder of the ubuntu project? Mark Shuttleworth 3 82.0%
Who is the quarterback for the green bay packers? Aaron Rodgers 3 81.1%
Panda is a national animal of which country? China 3 76.8%
Who came up with the theory of relativity? Albert Einstein 3 76.4%
When was the first star wars film released? 1977 3 71.4%
What is the most common blood type in sweden? A 7 70.6%
Who is regarded as the founder of psychoanalysis? Sigmund Freud 3 69.3%
Who took the first steps on the moon in 1969? Neil Armstrong 3 66.8%
Who is the largest supermarket chain in the uk? Tesco 3 65.3%
What is the meaning of shalom in english? peace 3 64.0%
Who was the author of the art of war? Sun Tzu 3 59.6%
Largest state in the us by land mass? California 7 59.2%
Green algae is an example of which type of reproduction? parthenogenesis 7 56.5%
Vikram samvat calender is official in which country? India 3 55.6%
Who is mostly responsible for writing the declaration of independence? Thomas Jefferson 3 53.3%
What us state forms the western boundary of montana? Montana 7 52.3%
Who plays ser davos in game of thrones? Peter Dinklage 7 52.1%
Who appoints the chair of the federal reserve system? Janet Yellen 7 51.5%
State the process that divides one nucleus into two genetically identical nuclei? mitosis 3 50.7%
Who won the most mvp awards in the nba? Michael Jordan 7 50.2%
What river is associated with the city of rome? the Tiber 3 48.6%
Who is the first president to be impeached? Andrew Johnson 3 48.3%
Who is the head of the department of homeland security 2017? John Kelly 3 47.0%
What is the name given to the common currency to the european union? Euro 3 46.8%
What was the emperor name in star wars? Palpatine 3 46.5%
Do you have to have a gun permit to shoot at a range? No 3 46.4%
Who proposed evolution in 1859 as the basis of biological development? Charles Darwin 3 45.7%
Nuclear power plant that blew up in russia? Chernobyl 3 45.7%
Who played john connor in the original terminator? Arnold Schwarzenegger 7 45.2%
Table 5.
The 30 most confident answers generated by GPT-2 on the development set of Natural Questions sorted by their probability
according to GPT-2. None of these questions appear in WebText according to the procedure described in Section 4.
(Conneau et al., 2017b). On the WMT-14 French-English
test set, GPT-2 is able to leverage its very strong English
language model to perform significantly better, achieving
11.5 BLEU. This outperforms several unsupervised machine
translation baselines from (Artetxe et al., 2017) and (Lample
et al., 2017) but is still much worse than the 33.5 BLEU of
the current best unsupervised machine translation approach
(Artetxe et al., 2019). Performance on this task was sur-
prising to us, since we deliberately removed non-English
webpages from WebText as a filtering step. In order to con-
firm this, we ran a byte-level language detector
2
on WebText
which detected only 10MB of data in the French language
which is approximately 500x smaller than the monolingual
French corpus common in prior unsupervised machine trans-
lation research.
3.8. Question Answering
A potential way to test what information is contained within
a language model is to evaluate how often it generates the
correct answer to factoid-style questions. Previous showcas-
ing of this behavior in neural systems where all information
is stored in parameters such as A Neural Conversational
Model (Vinyals & Le, 2015) reported qualitative results due
to the lack of high-quality evaluation datasets. The recently
introduced Natural Questions dataset (Kwiatkowski et al.,
2
https://github.com/CLD2Owners/cld2
2019) is a promising resource to test this more quantita-
tively. Similar to translation, the context of the language
model is seeded with example question answer pairs which
helps the model infer the short answer style of the dataset.
GPT-2 answers 4.1% of questions correctly when evalu-
ated by the exact match metric commonly used on reading
comprehension datasets like SQUAD.
3
As a comparison
point, the smallest model does not exceed the 1.0% accu-
racy of an incredibly simple baseline which returns the most
common answer for each question type (who, what, where,
etc...). GPT-2 answers 5.3 times more questions correctly,
suggesting that model capacity has been a major factor in
the poor performance of neural systems on this kind of task
as of yet. The probability GPT-2 assigns to its generated
answers is well calibrated and GPT-2 has an accuracy of
63.1% on the 1% of questions it is most confident in. The
30 most confident answers generated by GPT-2 on develop-
ment set questions are shown in Table 5. The performance
of GPT-2 is still much, much, worse than the 30 to 50%
range of open domain question answering systems which
hybridize information retrieval with extractive document
question answering (Alberti et al., 2019).
3
Alec, who previously thought of himself as good at random
trivia, answered 17 of 100 randomly sampled examples correctly
when tested in the same setting as GPT-2.
He actually only got 14 right but he
should have gotten those other 3
Language Models are Unsupervised Multitask Learners
PTB WikiText-2 enwik8 text8 Wikitext-103 1BW
Dataset train 2.67% 0.66% 7.50% 2.34% 9.09% 13.19%
WebText train 0.88% 1.63% 6.31% 3.94% 2.42% 3.75%
Table 6. Percentage of test set 8 grams overlapping with training sets.
4. Generalization vs Memorization
Recent work in computer vision has shown that common im-
age datasets contain a non-trivial amount of near-duplicate
images. For instance CIFAR-10 has 3.3% overlap between
train and test images (Barz & Denzler, 2019). This results in
an over-reporting of the generalization performance of ma-
chine learning systems. As the size of datasets increases this
issue becomes increasingly likely which suggests a similar
phenomena could be happening with WebText. Therefore it
is important to analyze how much test data also shows up in
the training data.
To study this we created Bloom filters containing 8-grams
of WebText training set tokens. To improve recall, strings
were normalized to contain only lower-cased alphanumeric
words with a single space as a delimiter. The Bloom filters
were constructed such that the false positive rate is upper
bounded by
1
10
8
. We further verified the low false positive
rate by generating 1M strings, of which zero were found by
the filter.
These Bloom filters let us calculate, given a dataset, the
percentage of 8-grams from that dataset that are also found
in the WebText training set. Table 6 shows this overlap anal-
ysis for the test sets of common LM benchmarks. Common
LM datasets’ test sets have between 1-6% overlap with Web-
Text train, with an average of overlap of 3.2%. Somewhat
surprisingly, many datasets have larger overlaps with their
own training splits, with an average of 5.9% overlap.
Our approach optimizes for recall, and while manual inspec-
tion of the overlaps shows many common phrases, there are
many longer matches that are due to duplicated data. This is
not unique to WebText. For instance, we discovered that the
test set of WikiText-103 has an article which is also in the
training dataset. Since there are only 60 articles in the test
set there is at least an overlap of 1.6%.
4
Potentially more
worryingly, 1BW has an overlap of nearly 13.2% with its
own training set according to our procedure.
For the Winograd Schema Challenge, we found only 10
schemata which had any 8-gram overlaps with the WebText
training set. Of these, 2 were spurious matches. Of the
remaining 8, only 1 schema appeared in any contexts that
4
A significant portion of additional overlap is due to editors
reusing some paragraphs across multiple articles with a shared
theme such as various battles in the Korean War.
gave away the answer.
For CoQA, about 15% of documents in the news domain
are already in WebText and the model performs about 3
F1 better on these. CoQAs development set metric reports
the average performance over 5 different domains and we
measure a gain of about 0.5-1.0 F1 due to overlap across the
various domains. However, no actual training questions or
answers are in WebText since CoQA was released after the
cutoff date for links in WebText.
On LAMBADA, the average overlap is 1.2%. GPT-2 per-
forms about 2 perplexity better on examples with greater
than 15% overlap. Recalculating metrics when excluding
all examples with any overlap shifts results from 8.6 to 8.7
perplexity and reduces accuracy from 63.2% to 62.9%. This
very small change in overall results is likely due to only 1
in 200 examples having significant overlap.
Overall, our analysis suggests that data overlap between
WebText training data and specific evaluation datasets pro-
vides a small but consistent benefit to reported results. How-
ever, for most datasets we do not notice significantly larger
overlaps than those already existing between standard train-
ing and test sets, as Table 6 highlights.
Understanding and quantifying how highly similar text im-
pacts performance is an important research question. Better
de-duplication techniques such as scalable fuzzy matching
could also help better answer these questions. For now, we
recommend the use of n-gram overlap based de-duplication
as an important verification step and sanity check during the
creation of training and test splits for new NLP datasets.
Another potential way of determining whether the perfor-
mance of WebText LMs is attributable to memorization is
inspecting their performance on their own held-out set. As
shown in Figure 4, performance on both the training and
test sets of WebText are similar and improve together as
model size is increased. This suggests even GPT-2 is still
underfitting on WebText in many ways.
GPT-2 is also able to write news articles about the discovery
of talking unicorns. An example is provided in Table 13.
5. Related Work
A significant portion of this work measured the performance
of larger language models trained on larger datasets. This
Language Models are Unsupervised Multitask Learners
Figure 4.
The performance of LMs trained on WebText as a func-
tion of model size.
is similar to the work of Jozefowicz et al. (2016) which
scaled RNN based language models on the 1 Billion Word
Benchmark. Bajgar et al. (2016) also previously improved
results on the Children’s Book Test by creating a much larger
training dataset out of Project Gutenberg to supplement the
standard training dataset. Hestness et al. (2017) conducted
a thorough analysis of how the performance of various deep
learning models changes as a function of both model capac-
ity and dataset size. Our experiments, while much noisier
across tasks, suggest similar trends hold for sub-tasks of an
objective and continue into the 1B+ parameter regime.
Interesting learned functionality in generative models
has been documented before such as the cells in an
RNN language model performing line-width tracking and
quote/comment detection Karpathy et al. (2015). More in-
spirational to our work was the observation of Liu et al.
(2018) that a model trained to generate Wikipedia articles
also learned to translate names between languages.
Previous work has explored alternative approaches to filter-
ing and constructing a large text corpus of web pages, such
as the iWeb Corpus (Davies, 2018).
There has been extensive work on pre-training methods
for language tasks. In addition to those mentioned in the
introduction, GloVe (Pennington et al., 2014) scaled word
vector representation learning to all of Common Crawl. An
influential early work on deep representation learning for
text was Skip-thought Vectors (Kiros et al., 2015). McCann
et al. (2017) explored the use of representations derived from
machine translation models and Howard & Ruder (2018)
improved the RNN based fine-tuning approaches of (Dai
& Le, 2015). (Conneau et al., 2017a) studied the transfer
performance of representations learned by natural language
inference models and (Subramanian et al., 2018) explored
large-scale multitask training.
(Ramachandran et al., 2016) demonstrated that seq2seq mod-
els benefit from being initialized with pre-trained language
models as encoders and decoders. More recent work has
shown that LM pre-training is helpful when fine-tuned for
difficult generation tasks like chit-chat dialog and dialog
based question answering systems as well (Wolf et al., 2019)
(Dinan et al., 2018).
6. Discussion
Much research has been dedicated to learning (Hill et al.,
2016), understanding (Levy & Goldberg, 2014), and criti-
cally evaluating (Wieting & Kiela, 2019) the representations
of both supervised and unsupervised pre-training methods.
Our results suggest that unsupervised task learning is an
additional promising area of research to explore. These
findings potentially help explain the widespread success of
pre-training techniques for down-stream NLP tasks as we
show that, in the limit, one of these pre-training techniques
begins to learn to perform tasks directly without the need
for supervised adaption or modification.
On reading comprehension the performance of GPT-2 is
competitive with supervised baselines in a zero-shot setting.
However, on other tasks such as summarization, while it
is qualitatively performing the task, its performance is still
only rudimentary according to quantitative metrics. While
suggestive as a research result, in terms of practical applica-
tions, the zero-shot performance of GPT-2 is still far from
use-able.
We have studied the zero-shot performance of WebText
LMs on many canonical NLP tasks, but there are many addi-
tional tasks that could be evaluated. There are undoubtedly
many practical tasks where the performance of GPT-2 is
still no better than random. Even on common tasks that we
evaluated on, such as question answering and translation,
language models only begin to outperform trivial baselines
when they have sufficient capacity.
While zero-shot performance establishes a baseline of the
potential performance of GPT-2 on many tasks, it is not
clear where the ceiling is with finetuning. On some tasks,
GPT-2’s fully abstractive output is a significant departure
from the extractive pointer network (Vinyals et al., 2015)
based outputs which are currently state of the art on many
question answering and reading comprehension datasets.
Given the prior success of fine-tuning GPT, we plan to in-
vestigate fine-tuning on benchmarks such as decaNLP and
GLUE, especially since it is unclear whether the additional
Language Models are Unsupervised Multitask Learners
training data and capacity of GPT-2 is sufficient to over-
come the inefficiencies of uni-directional representations
demonstrated by BERT (Devlin et al., 2018).
7. Conclusion
When a large language model is trained on a sufficiently
large and diverse dataset it is able to perform well across
many domains and datasets. GPT-2 zero-shots to state of
the art performance on 7 out of 8 tested language model-
ing datasets. The diversity of tasks the model is able to
perform in a zero-shot setting suggests that high-capacity
models trained to maximize the likelihood of a sufficiently
varied text corpus begin to learn how to perform a surprising
amount of tasks without the need for explicit supervision.
5
Acknowledgements
Thanks to everyone who wrote the text, shared the links,
and upvoted the content in WebText. Many millions of
people were involved in creating the data that GPT-2 was
trained on. Also thanks to all the Googlers who helped us
with training infrastructure, including Zak Stone, JS Riehl,
Jonathan Hseu, Russell Power, Youlong Cheng, Noam
Shazeer, Solomon Boulos, Michael Banfield, Aman Gupta,
Daniel Sohn, and many more. Finally thanks to the people
who gave feedback on drafts of the paper: Jacob Steinhardt,
Sam Bowman, Geoffrey Irving, and Madison May.
References
Al-Rfou, R., Choe, D., Constant, N., Guo, M., and Jones, L.
Character-level language modeling with deeper self-attention.
arXiv preprint arXiv:1808.04444, 2018.
Alberti, C., Lee, K., and Collins, M. A bert baseline for the natural
questions. arXiv preprint arXiv:1901.08634, 2019.
Alcorn, M. A., Li, Q., Gong, Z., Wang, C., Mai, L., Ku, W.-S., and
Nguyen, A. Strike (with) a pose: Neural networks are easily
fooled by strange poses of familiar objects. arXiv preprint
arXiv:1811.11553, 2018.
Amodei, D., Ananthanarayanan, S., Anubhai, R., Bai, J., Batten-
berg, E., Case, C., Casper, J., Catanzaro, B., Cheng, Q., Chen,
G., et al. Deep speech 2: End-to-end speech recognition in
english and mandarin. In International Conference on Machine
Learning, pp. 173–182, 2016.
Artetxe, M., Labaka, G., Agirre, E., and Cho, K. Unsupervised
neural machine translation. arXiv preprint arXiv:1710.11041,
2017.
Artetxe, M., Labaka, G., and Agirre, E. An effective ap-
proach to unsupervised machine translation. arXiv preprint
arXiv:1902.01313, 2019.
5
Preliminary code for downloading and using the small model
is available at https://github.com/openai/gpt-2
Ba, J. L., Kiros, J. R., and Hinton, G. E. Layer normalization.
arXiv preprint arXiv:1607.06450, 2016.
Bajgar, O., Kadlec, R., and Kleindienst, J. Embracing data abun-
dance: Booktest dataset for reading comprehension. arXiv
preprint arXiv:1610.00956, 2016.
Barz, B. and Denzler, J. Do we train on test data? purging cifar of
near-duplicates. arXiv preprint arXiv:1902.00423, 2019.
Bengio, Y., Ducharme, R., Vincent, P., and Jauvin, C. A neural
probabilistic language model. Journal of machine learning
research, 3(Feb):1137–1155, 2003.
Bowman, S. R., Pavlick, E., Grave, E., Van Durme, B., Wang, A.,
Hula, J., Xia, P., Pappagari, R., McCoy, R. T., Patel, R., et al.
Looking for elmo’s friends: Sentence-level pretraining beyond
language modeling. arXiv preprint arXiv:1812.10860, 2018.
Caruana, R. Multitask learning. Machine learning , 28(1):41–75,
1997.
Chelba, C., Mikolov, T., Schuster, M., Ge, Q., Brants, T., Koehn,
P., and Robinson, T. One billion word benchmark for measur-
ing progress in statistical language modeling. arXiv preprint
arXiv:1312.3005, 2013.
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu,
K., and Kuksa, P. Natural language processing (almost) from
scratch. Journal of Machine Learning Research, 12(Aug):2493–
2537, 2011.
Conneau, A., Kiela, D., Schwenk, H., Barrault, L., and Bor-
des, A. Supervised learning of universal sentence represen-
tations from natural language inference data. arXiv preprint
arXiv:1705.02364, 2017a.
Conneau, A., Lample, G., Ranzato, M., Denoyer, L., and J
´
egou,
H. Word translation without parallel data. arXiv preprint
arXiv:1710.04087, 2017b.
Dai, A. M. and Le, Q. V. Semi-supervised sequence learning. In
Advances in neural information processing systems, pp. 3079–
3087, 2015.
Dai, Z., Yang, Z., Yang, Y., Cohen, W. W., Carbonell, J., Le,
Q. V., and Salakhutdinov, R. Transformer-xl: Attentive lan-
guage models beyond a fixed-length context. arXiv preprint
arXiv:1901.02860, 2019.
Davies, M. The 14 billion word iweb corpus.
https://corpus.byu.edu/iWeb/, 2018.
Dehghani, M., Gouws, S., Vinyals, O., Uszkoreit, J., and Kaiser,
Ł
. Universal transformers. arXiv preprint arXiv:1807.03819,
2018.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. Bert: Pre-
training of deep bidirectional transformers for language under-
standing. arXiv preprint arXiv:1810.04805, 2018.
Dinan, E., Roller, S., Shuster, K., Fan, A., Auli, M., and Weston,
J. Wizard of wikipedia: Knowledge-powered conversational
agents. arXiv preprint arXiv:1811.01241, 2018.
Fan, A., Lewis, M., and Dauphin, Y. Hierarchical neural story
generation. arXiv preprint arXiv:1805.04833, 2018.
Language Models are Unsupervised Multitask Learners
Finn, C., Abbeel, P., and Levine, S. Model-agnostic meta-
learning for fast adaptation of deep networks. arXiv preprint
arXiv:1703.03400, 2017.
Gehrmann, S., Deng, Y., and Rush, A. M. Bottom-up abstractive
summarization. arXiv preprint arXiv:1808.10792, 2018.
Gillick, D., Brunk, C., Vinyals, O., and Subramanya, A. Mul-
tilingual language processing from bytes. arXiv preprint
arXiv:1512.00103, 2015.
Gong, C., He, D., Tan, X., Qin, T., Wang, L., and Liu, T.-Y. Frage:
frequency-agnostic word representation. In Advances in Neural
Information Processing Systems, pp. 1341–1352, 2018.
Grave, E., Joulin, A., and Usunier, N. Improving neural
language models with a continuous cache. arXiv preprint
arXiv:1612.04426, 2016.
He, K., Zhang, X., Ren, S., and Sun, J. Identity mappings in deep
residual networks. In European conference on computer vision,
pp. 630–645. Springer, 2016.
Hestness, J., Narang, S., Ardalani, N., Diamos, G., Jun, H., Kian-
inejad, H., Patwary, M., Ali, M., Yang, Y., and Zhou, Y. Deep
learning scaling is predictable, empirically. arXiv preprint
arXiv:1712.00409, 2017.
Hill, F., Bordes, A., Chopra, S., and Weston, J. The goldilocks
principle: Reading children’s books with explicit memory rep-
resentations. arXiv preprint arXiv:1511.02301, 2015.
Hill, F., Cho, K., and Korhonen, A. Learning distributed repre-
sentations of sentences from unlabelled data. arXiv preprint
arXiv:1602.03483, 2016.
Hoang, L., Wiseman, S., and Rush, A. M. Entity tracking im-
proves cloze-style reading comprehension. arXiv preprint
arXiv:1810.02891, 2018.
Howard, J. and Ruder, S. Universal language model fine-tuning for
text classification. In Proceedings of the 56th Annual Meeting
of the Association for Computational Linguistics (Volume 1:
Long Papers), volume 1, pp. 328–339, 2018.
Jelinek, F. and Mercer, R. L. Interpolated estimation of markov
source parameters from sparse data. In Proceedings of the
Workshop on Pattern Recognition in Practice, Amsterdam, The
Netherlands: North-Holland, May., 1980.
Jia, R. and Liang, P. Adversarial examples for evaluating read-
ing comprehension systems. arXiv preprint arXiv:1707.07328,
2017.
Jozefowicz, R., Vinyals, O., Schuster, M., Shazeer, N., and Wu,
Y. Exploring the limits of language modeling. arXiv preprint
arXiv:1602.02410, 2016.
Kaiser, L., Gomez, A. N., Shazeer, N., Vaswani, A., Parmar, N.,
Jones, L., and Uszkoreit, J. One model to learn them all. arXiv
preprint arXiv:1706.05137, 2017.
Karpathy, A., Johnson, J., and Fei-Fei, L. Visualizing and under-
standing recurrent networks. arXiv preprint arXiv:1506.02078,
2015.
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins,
G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-
Barwinska, A., et al. Overcoming catastrophic forgetting in
neural networks. Proceedings of the national academy of sci-
ences, pp. 201611835, 2017.
Kiros, R., Zhu, Y., Salakhutdinov, R. R., Zemel, R., Urtasun, R.,
Torralba, A., and Fidler, S. Skip-thought vectors. In Advances
in neural information processing systems, pp. 3294–3302, 2015.
Krizhevsky, A., Sutskever, I., and Hinton, G. E. Imagenet classifi-
cation with deep convolutional neural networks. In Advances in
neural information processing systems, pp. 1097–1105, 2012.
Kwiatkowski, T., Palomaki, J., Rhinehart, O., Collins, M., Parikh,
A., Alberti, C., Epstein, D., Polosukhin, I., Kelcey, M., Devlin,
J., et al. Natural questions: a benchmark for question answering
research. 2019.
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., and Gershman, S. J.
Building machines that learn and think like people. Behavioral
and Brain Sciences, 40, 2017.
Lample, G., Conneau, A., Denoyer, L., and Ranzato, M. Unsu-
pervised machine translation using monolingual corpora only.
arXiv preprint arXiv:1711.00043, 2017.
Levesque, H., Davis, E., and Morgenstern, L. The winograd
schema challenge. In Thirteenth International Conference on
the Principles of Knowledge Representation and Reasoning,
2012.
Levy, O. and Goldberg, Y. Neural word embedding as implicit ma-
trix factorization. In Advances in neural information processing
systems, pp. 2177–2185, 2014.
Liu, P. J., Saleh, M., Pot, E., Goodrich, B., Sepassi, R., Kaiser, L.,
and Shazeer, N. Generating wikipedia by summarizing long
sequences. arXiv preprint arXiv:1801.10198, 2018.
McCann, B., Bradbury, J., Xiong, C., and Socher, R. Learned
in translation: Contextualized word vectors. In Advances in
Neural Information Processing Systems, pp. 6294–6305, 2017.
McCann, B., Keskar, N. S., Xiong, C., and Socher, R. The natural
language decathlon: Multitask learning as question answering.
arXiv preprint arXiv:1806.08730, 2018.
Merity, S., Xiong, C., Bradbury, J., and Socher, R. Pointer sentinel
mixture models. arXiv preprint arXiv:1609.07843, 2016.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean,
J. Distributed representations of words and phrases and their
compositionality. In Advances in neural information processing
systems, pp. 3111–3119, 2013.
Nallapati, R., Zhou, B., Gulcehre, C., Xiang, B., et al. Abstrac-
tive text summarization using sequence-to-sequence rnns and
beyond. arXiv preprint arXiv:1602.06023, 2016.
Paperno, D., Kruszewski, G., Lazaridou, A., Pham, Q. N., Bernardi,
R., Pezzelle, S., Baroni, M., Boleda, G., and Fern
´
andez, R. The
lambada dataset: Word prediction requiring a broad discourse
context. arXiv preprint arXiv:1606.06031, 2016.
Pennington, J., Socher, R., and Manning, C. Glove: Global vectors
for word representation. In Proceedings of the 2014 conference
on empirical methods in natural language processing (EMNLP),
pp. 1532–1543, 2014.
Language Models are Unsupervised Multitask Learners
Peters, M. E. and Lecocq, D. Content extraction using diverse fea-
ture sets. In Proceedings of the 22nd International Conference
on World Wide Web, pp. 89–90. ACM, 2013.
Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark, C.,
Lee, K., and Zettlemoyer, L. Deep contextualized word repre-
sentations. arXiv preprint arXiv:1802.05365, 2018.
Radford, A., Jozefowicz, R., and Sutskever, I. Learning to
generate reviews and discovering sentiment. arXiv preprint
arXiv:1704.01444, 2017.
Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I.
Improving language understanding by generative pre-training.
2018.
Ramachandran, P., Liu, P. J., and Le, Q. V. Unsupervised pre-
training for sequence to sequence learning. arXiv preprint
arXiv:1611.02683, 2016.
Recht, B., Roelofs, R., Schmidt, L., and Shankar, V. Do
cifar-10 classifiers generalize to cifar-10? arXiv preprint
arXiv:1806.00451, 2018.
Reddy, S., Chen, D., and Manning, C. D. Coqa: A conversational
question answering challenge. arXiv preprint arXiv:1808.07042,
2018.
Schwartz, R., Sap, M., Konstas, I., Zilles, L., Choi, Y., and Smith,
N. A. Story cloze task: Uw nlp system. In Proceedings of the
2nd Workshop on Linking Models of Lexical, Sentential and
Discourse-level Semantics, pp. 52–55, 2017.
See, A., Liu, P. J., and Manning, C. D. Get to the point: Sum-
marization with pointer-generator networks. arXiv preprint
arXiv:1704.04368, 2017.
Sennrich, R., Haddow, B., and Birch, A. Neural machine trans-
lation of rare words with subword units. arXiv preprint
arXiv:1508.07909, 2015.
Subramanian, S., Trischler, A., Bengio, Y., and Pal, C. J. Learning
general purpose distributed sentence representations via large
scale multi-task learning. arXiv preprint arXiv:1804.00079,
2018.
Sutskever, I., Vinyals, O., and Le, Q. V. Sequence to sequence
learning with neural networks. In Advances in neural informa-
tion processing systems, pp. 3104–3112, 2014.
Sutskever, I., Jozefowicz, R., Gregor, K., Rezende, D., Lillicrap,
T., and Vinyals, O. Towards principled unsupervised learning.
arXiv preprint arXiv:1511.06440, 2015.
Trichelair, P., Emami, A., Cheung, J. C. K., Trischler, A., Sule-
man, K., and Diaz, F. On the evaluation of common-sense
reasoning in natural language understanding. arXiv preprint
arXiv:1811.01778, 2018.
Trinh, T. H. and Le, Q. V. A simple method for commonsense
reasoning. arXiv preprint arXiv:1806.02847, 2018.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L.,
Gomez, A. N., Kaiser,
Ł
., and Polosukhin, I. Attention is
all you need. In Advances in Neural Information Processing
Systems, pp. 5998–6008, 2017.
Vinyals, O. and Le, Q. A neural conversational model. arXiv
preprint arXiv:1506.05869, 2015.
Vinyals, O., Fortunato, M., and Jaitly, N. Pointer networks. In
Advances in Neural Information Processing Systems, pp. 2692–
2700, 2015.
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., and Bow-
man, S. R. Glue: A multi-task benchmark and analysis
platform for natural language understanding. arXiv preprint
arXiv:1804.07461, 2018.
Weston, J. E. Dialog-based language learning. In Advances in
Neural Information Processing Systems, pp. 829–837, 2016.
Wieting, J. and Kiela, D. No training required: Exploring
random encoders for sentence classification. arXiv preprint
arXiv:1901.10444, 2019.
Wolf, T., Sanh, V., Chaumond, J., and Delangue, C. Transfer-
transfo: A transfer learning approach for neural network based
conversational agents. arXiv preprint arXiv:1901.08149, 2019.
Yogatama, D., d’Autume, C. d. M., Connor, J., Kocisky, T.,
Chrzanowski, M., Kong, L., Lazaridou, A., Ling, W., Yu, L.,
Dyer, C., et al. Learning and evaluating general linguistic intel-
ligence. arXiv preprint arXiv:1901.11373, 2019.
Language Models are Unsupervised Multitask Learners
8. Appendix A: Samples
8.1. Model capacity
To complement the reported perplexity gains of bigger LMs on
WebText show in Figure 4, Tables 7 through 11 show side-by-side
completions of the smallest WebText LM and GPT-2 on random
unseen WebText test set articles.
8.2. Text Memorization
We observe some memorizing behavior in GPT-2 on longer strings
that are repeated many times in the dataset such as famous quotes
or speeches. For example, when conditioned on the first sentence
and a half of the Gettysburg Address (which occurs approximately
40 times throughout WebText), an argmax decode from GPT-2
recovers the speech. Even when sampling without truncation, we
find that the model copies the speech for awhile before drifting,
albeit in a similar style. It typically drifts within 100-200 tokens,
and displays widening diversity once it drifts.
To quantify how often exact memorization shows up in samples,
we generated samples from GPT-2 conditioned on WebText test
set articles and compared the overlap rates of GPT-2’s generations
to the overlap rates of the ground-truth completions. The results of
this analysis are shown below and suggest that GPT-2 repeats text
from the training set less often then the baseline rate of held-out
articles.
Figure 5.
CDF of percentage 8-gram overlap with WebText train-
ing set, for both WebText test set and samples (conditioned on
WebText test set, with top-
k
truncated random sampling with
k = 40
). Most samples have less than 1% overlap, including over
30% of samples with no overlap, whereas the median for test set is
2.6% overlap.
8.3. Diversity
Table 12 shows multiple completions of the same random WebText
test set context, showing the diversity of completions with standard
sampling settings.
8.4. Robustness
Table 13 shows the previously mentioned talking unicorns news
article. We find the model to be capable of handling out of dis-
tribution contexts, but the quality of these samples is generally
lower.
Language Models are Unsupervised Multitask Learners
Context (WebText test)
Outfit 1: Typical
This pairing was the first outfit I thought of when I bought the shoes. It’s like a summer version of this
Jake Grantham outfit; in fact, my shoes are close to the colors of his Nike Racers! Instead of a heavy Harris Tweed
jacket and denim shirt, I’m wearing a cotton DB jacket and and a linen shirt. Both fabrics (in these colors) are an
absolute must for summer, as they go with both dark and and light pants! As you can see, they pair wonderfully
with the dark jeans and shoes. It’s a pseudo menswear/prep outfit.
Overall, this is a very casual outfit which is why I paired my sneakers with it. I’m not about wearing a
full wool suit with sneakers (as GQ shows a lot) but I’m definitely open to keeping things casual, like this cotton
DB. Casual fabrics are key to pulling off your sneakers in a dressed down menswear outfit. I’d even suggest to
wear these sneakers with a khaki chino suit or a white linen suit. Just be sure to ditch the tie or wear a tee or polo;
wearing a tie with sneakers is a bit too much
Small model completion
.
Outfit 2: High End
This outfit was just in the eye of the storm. It’s a
hybrid of what ASICS does with streetwear (like J. Crew)
and what J. Crew does with casual pieces that are a bit
more traditional. Again, as I mentioned before, it’s a low
budget item and they can be very expensive. These will
definitely be an issue if you’re trying to work as a casual
sneakermaker, and that’s OK but don’t hesitate if you
want to try the lower price versions of their outfits!
This version features a very low cut-cut neckline
(which is not a bad thing), slightly longer legs, and a pair
of long legs to keep things nice. My preferred color of
color these items are blue-gray (which is my first colors,
as they are a light white), or black (which is my favorite
of my designs). As always, my pick of colors from this
store have nothing to do with that other than the de-
sign. For a high end, I would recommend this to everyone!
Outfit 3: High Light
This was a ”high light” look while I had a chance
to get some color. I think the color is based on
GPT-2 completion
for me!
Outfit 2: Casual Jacket
I know that some people might be opposed to wearing
sneakers in a turtleneck, but I wanted to be true to myself,
so I went with a slim fitting turtleneck in a color more
similar to my favorite color of the day. Overall, it’s a
classic turtleneck, with nothing too flashy. I did swap my
shoes for these ASICS Tiger Killshots and I wish I hadn’t
did that. The shoe itself is very comfortable, however,
I found that wearing them in these sneakers made my
feet look a bit larger. I’d wear them with denim, chinos,
whatever, but would never recommend wearing them
alone. There are just too many variables involved.
Outfit 3: Dress
This is the final outfit I wore the ASICS Tiger
Killshots. This version of the ASICS is a much more
modern and streamlined version. The shoe has the most
modern and minimal design that I can think of! With a lot
of black, these sneakers look very cool. They’ll definitely
be very comfortable for summer.
To me, wearing them with a suit is the easiest way
to have a good pair of sneaker shoes. That’s pretty much
it! If
Table 7.
Random unseen contexts (top), and non-cherry-picked completions from both the smallest (left) and largest (right) models.
Contexts are 768 tokens, with approximately 256 tokens worth of paragraphs shown. Completions are 256 tokens and fully shown. Top-
k
random sampling with k = 40 was used for generation.
Language Models are Unsupervised Multitask Learners
Context (WebText test)
Some of the most glorious historical attractions in Spain date from the period of Muslim rule, including The
Mezquita, built as the Great Mosque of C
´
ordoba and the Medina Azahara, also in C
´
ordoba and now in ruins but
still visitable as such and built as the Madinat al-Zahra, the Palace of al-Andalus; and the Alhambra in Granada, a
splendid, intact palace. There are also two synagogues still standing that were built during the era of Muslim Spain:
Santa Mar
´
ıa la Blanca in Toledo and the Synagogue of C
´
ordoba, in the Old City.
Reconquista and Imperial era [ edit ]
Small model completion
The era of Muslim Spain is very different from that of
other parts of the world, with a rather extensive medieval
history. While the Spanish Empire existed during the
second half of the 9th century BC it was quite slowly
transformed to a small, secularized empire by 1150 A.D.,
in a much more prosperous and diverse state. In 1050 CE
the emperor of Naples, at least partially in characterised by
a series of wars and invasions from the north-western coast
to the western coast of the region (e.g., on the Danube
River which extended all the way from Gibraltar into the
south of Spain, but was quickly destroyed on the same
day by the Roman army), assumed the title of Emperor
of Naples. He made a large contribution over the last few
decades to restore and maintain the empire, the first of
which to the west. His successors were Ferdinand, who
took power from the Papal Throne in 1148, while the first
imperial successor was King Alfred II of Sweden. The
empire was again made one of the few independent states
in the European Continent and although no more than 200
million people lived on its territory during this period the
people had much to say to the Emperor. From 1720 on the
Muslims made up half the population of Spain.
GPT-2 completion
The Reconquista (literally, ”The return to the country”)
of 1092 and 1095 saw the establishment in the Iberian
peninsula of a united kingdom of Castile and Aragon in
which a Muslim dynasty would exist. This was the last
time in Europe where Muslim rule would be considered
”native” and the last occasion where Christian-inflamed
and organized conflicts would make themselves known in
Europe.
The reign of Muslim Moors in Spain is often re-
ferred to, especially among non-Muslims, as the
”Conquest of Granada” and Spain is sometimes referred
to as Spain in ”Granada”. The last years of Muslim rule
were followed by the Spanish Reconquista where the
Muslims were driven or compelled out of the Iberian
Peninsula with the victory of Ferdinand IX. in the Battle
of Poitiers in March 1189 and the conquest of Jerusalem
in 1187 by Saladin.
The Reconquista period in Spain came to an end
when the Moors retreated north. However the Moorish
rule would not be fully replaced by the Christian rule until
the Reconquista era resumed when the Reconquista lasted
from 1229 to 1492.
The last Muslim state in Spain, Granada, was con-
quered by the
Table 8.
Random unseen contexts (top), and non-cherry-picked completions from both the smallest (left) and largest (right) models.
Contexts are 768 tokens, with approximately 256 tokens worth of paragraphs shown. Completions are 256 tokens and fully shown. Top-
k
random sampling with k = 40 was used for generation.
Language Models are Unsupervised Multitask Learners
Context (WebText test)
Pinky
The pink ghost’s AI is designed to ”feel” opposite of the red ghost’s behavior. Pinky actually attempts to
get out in front of Pac-Man. This is accomplished by setting the target 4 tiles ahead of Pac-Man’s current location in
the direction that Pac-Man is travelling. One exception to this is when Pac-Man is traveling up. Due to an overflow
bug in the code, the calculation includes a left offset equal to the expected up offset.
The use of Pac-Man’s expected position allows the player to exploit the navigation code for Pinky. This was most
often seen in situations where Pac-Man and Pinky are in close proximity to each other. For example, if they were
headed toward one another, once they are less than 4 tiles apart, the target tile is now behind Pinky. If he encounters
an intersection while in this configuration, he will attempt to double-back (by turning) in order to reach the target tile.
Player’s would often perform what was referred to as a ”head fake” by changing directions rapidly just
as Pinky was approaching an intersection. By displacing the target tile just as Pinky
Small model completion
was coming to the left, they would get the ”correct”
direction.
If the location is actually farther from what Pac-
Man was at, the player’s will not respond to the ghost’s
movement, and the ghost will instead return to the player’s
current location. This is because Pac-Man does not move
by turning.
The ghost will have to go to another location of
some kind to return where he began, and this is achieved
in many ways. It will often simply leave the location, as
for example if Pac-Man did jump off a cliff, he would
return to the spot where he was before vanishing into
space. There is a good number of possibilities these ghost
paths may be possible, but the most obvious ones are if
the player wants to avoid getting caught up in the game,
with a goal as easy as keeping Pac-Man in the game for
1/3 as long as he was.
Chasing
In Pac-Man’s case, this would be similar to how
Pac-Man can ”catch up” in a fight.
A ghost could chase through enemies. This is use-
ful for many reasons, both for Pac-Man and his AI.
Unlike most Ghost games, this ghost simply travels in the
direction from
GPT-2 completion
was about to cross the intersection, Pac-Man would be
able to dodge the ghosts’ projectiles and return to the safe
location of the safe house.
Buster
The yellow ghost’s AI is specifically designed to
try to avoid Pac-Man from leaving the safe house. The
player must press the SPACE bar to reveal the safe
house’s doors, then go into them to find the yellow ghost
hiding in the next room.
The player must also note that the ghost will not
attempt to exit the safe house if it sees Pac-Man already
there. In fact, the ghost will not attempt an exit in the first
place unless they’re already in the safe house.
These two behaviors are based on the fact that the
player does not always attempt to reach the safe house
before Pac-Man (or Blinky).
The player must also beware of this glitch if a
ghost (or another player) is already in the safe house
and attempting to get out as soon as Pac-Man or Blinky
is gone. At this point, the ghost will attempt to exit
the safe house in a similar manner as it is in ”Chase” mode.
Pac-Man
There are some ghosts that follow Pac-Man. These ghosts
are
Table 9.
Random unseen contexts (top), and non-cherry-picked completions from both the smallest (left) and largest (right) models.
Contexts are 768 tokens, with approximately 256 tokens worth of paragraphs shown. Completions are 256 tokens and fully shown. Top-
k
random sampling with k = 40 was used for generation.
Language Models are Unsupervised Multitask Learners
Context (WebText test)
Corporal Michael P. Goeldin was an unskilled laborer from Ireland when he enlisted in Company A in November
1860. Goldein survived the war. Corporal Patrick O’Neal, also from Ireland, first enlisted in 1854 and served with
Company L, 3d U.S. Artillery, in Oregon. He returned to the East Coast and enlisted in the company in 1860.
O’Neal served until 1874, when he was named superintendent of the National Cemetery at Willets Point, New York.
Corporal Benjamin Browne was a shoemaker from Orange County, New York. In August 1862, he enlisted in the
newly formed 124th New York Volunteers, and was one of sixty-one men who transferred into Company A that
October. Browne reenlisted in the company in February 1864 while it was camped at Brandy Station. He returned
to civilian life after completing his enlistment in 1867.
On 10 June, Artificer William Collins was promoted to corporal, probably to fill a combat leadership
void for the crossing of the James River. Collins’s service record does not reflect the qualities he demonstrated to
earn this promotion, but he had obviously overcome some serious problems. Born in Sacketts Harbor, New York,
Collins enlisted in the company in December 1853 at the age of twenty-two, and reenlisted in December 1858. Just
a month before the war began in April 1861, Collins went ”over the hill” and was not caught until three years later.
Returned to the company on 22 March 1864, he was tried
Small model completion
on military charges and was discharged from the U.S.
Military. He returned to the company, and was assigned
to Division of Internal Affairs during the Civil War.
As an engineer, Collins also had training in engi-
neering from his native New York. After graduate studies
in the Union College of Engineering, he was employed
in the United States Army for two years at the same
time. There he worked under Captain Daniel Pfeiffer
and was promoted to colonel in May 1870, three years
after Captain Pfeiffer’s enlistment. Collins returned
home to England after completing his first year in the
First Division to serve the next two years in the Army
Corps of Engineers, and by his second year, he began to
experience his service duty.¡—endoftext—¿MOSCOW,
July 5 (Reuters) - U.K.-based Russian state media and
government sources and experts say Moscow has sought
to downplay the ”military buildup” of the Syrian oppo-
sition while maintaining support for President Donald
Trump’s plan to build a wall along the U.S.-Mexico
border, calling it a ”military coup”.
U.S.-led Syria war veteran and Syria envoy Michael
Flynn (R), right, gestures with U.S.-based Syrian political
analyst Ahmet Koryat, with
GPT-2 completion
in June of that year. Sentenced to be shot for desertion, he
remained on active duty.
Captain James J. Dall was born in Virginia in 1829 during
the Revolution and joined his father in the Army in 1836.
He served with the 2d Virginia Infantry until 1845, when
he went to the Western Reserve Regiment; and, on his
discharge, he reenlisted with Company I, 25th New York
Volunteers (which in 1845 numbered about 4,500 men).
One of his brothers, James, served with his company
in the same capacity in the same brigade. While the
regiment remained in Connecticut through 1846, Captain
Dall served with Company I at various times before
his discharge. A veteran of the Mexican War, he had
served two years on the staff of Brigadier General John J.
Sullivan at San Antonio. During the Peninsula Campaign
of 1863, he commanded a company of twenty-three and
was in charge of the battery of fifty-four heavy mortars
and the gunboat Pinta of the Battery, a gunboat and
several other boats. Captain Dall was on active duty with
Company I in the fall of 1865. Two years later, he was
promoted to second lieutenant.
Captain John A. O’
Table 10.
Random unseen contexts (top), and non-cherry-picked completions from both the smallest (left) and largest (right) models.
Contexts are 768 tokens, with approximately 256 tokens worth of paragraphs shown. Completions are 256 tokens and fully shown. Top-
k
random sampling with k = 40 was used for generation.
Language Models are Unsupervised Multitask Learners
Context (WebText test)
Xmonad, Xresources, and Fonts
Xresources can be an absolute pain (they were for me). So, this a bit of help:
First of all, the way that fonts are generally handled in Xmonad is through XFT . Xft is the X FreeType
Interface library and is a library that handles the actual rendering of fonts in Xmonad/urxvt (Xresources specifies
a bunch of resources to things that launch under the X window manager. Settings for individual programs are
specified in Xresources like so: Urxvt.background:background settins here ).
Anyway, fonts are set using this syntax in Xresources :
urxvt*font:xft:your font name goes here:size=the size of the font goes here,xft:fall-back font 1 goes here
So, thats the most basic part of fonts in urxvt specified via Xresources.
Fixing /.ssh/ Permissions in Cygwin
In Cygwin on Windows I found that I could not set the permissions of my /.ssh/ folder to be 0600, as is
required for ssh to allow you to use keys. The symptom I had was that I no matter what I did, it always modfied the
owner and the group
Small model completion
was set to /.ssh/ , which was the way things were. This
could not be fixed completely. I found I could make that
much more workable with just typing the username with
your mouse then trying to set permissions to be ”0600”
for the group, which is what my system is now configured
to do.
However, as of Cygwin 7 on Linux I have found
that doing so is now ”unnecessary” for my system! I also
installed a package called chroot. It does that by allowing
you to install/upgrade to your system directory.
So, as of Windows 8 and up, installing that can re-
quire chroot, so I tried and tried and found that it wasn’t
so bad when installing the package on Linux and then
uninstalling it. As of my last write-up on Cygwin, the
script just does its job (the sudo commands do the rest), if
you try it you will have to log the change.
If that wasn’t confusing enough, this is what I tried and
just went back to my /.ssh/ directory and the chroot script.
I found I was finally running out of permissions (because
i forgot to create a symlink to ssh from the file system and
GPT-2 completion
, making these files read-only. I tried just starting ssh
and using a program like passwd which modifies the
owner/group to 0644 permissions, but it didn’t work. It
was annoying but not nearly as bad as it could be, just
annoying to have to fiddle with permissions.
So I decided to do this as the reverse:
For each .ssh folder in your Cygwin directory, set
the permission of each file and directory to 2644. Then
start ssh with: ssh-add /.ssh/
The first time you connect, your permissions of
the /.ssh folder that you just had in Cygwin will be
set to 0600. Then every time you connect again, your
permissions will change to 0644.
Makefile for Xresources as used in /.xsessionrc
These are made in my .xsessionrc :
.xprofile: .xsession-errors: color=””
For example:
.xprofile: color=blue
As the colors are stored in a separate file, the color in your
/.xsession-errors will look like cyan instead of blue.
This is mostly because, as of now, only the color
is assigned to your
Table 11.
Random unseen contexts (top), and non-cherry-picked completions from both the smallest (left) and largest (right) models.
Contexts are 768 tokens, with approximately 256 tokens worth of paragraphs shown. Completions are 256 tokens and fully shown. Top-
k
random sampling with k = 40 was used for generation.
Language Models are Unsupervised Multitask Learners
Context (WebText test)
Aaron loves mint chocolate cake, but he requires that it be paired with mini chocolate chips, so I threw some of
those in between the layers. I also had a few Peppermint Jo Jos on hand so I crushed them up and threw some of
those in along with some crushed meringue cookies because, why not? It’s a total smorgasbord of minty chocolate
chippy cookie crunchy goodness. I didn’t measure how much of each topping I used, but after I tasted the finished
product, I wish I had added more. You can add anything you want- crushed candy canes, peppermint bark, etc. And
don’t be afraid to use a heavy hand. Texture = good.
If you don’t have 7-inch cake pans, you can get 3 shorter 8-inch layers out of this
GPT-2 Completion 1
recipe. I made the longer version,
then used the pan for a layer of
buttercream frosting and stuck 1/3
of it into the fridge for a frosting
topper. Just make sure you have all
of this frosting prepared earlier than
you make this cake and you won’t
have to wait on it as your egg whites
sit on the bottom of your mixing
bowl.
You want the egg whites whipped
to stiff peaks? Then put your cake
dish over a pot of simmering water
and heat it up until it’s just barely
warm. It doesn’t take more than 3-5
minutes; just enough to get it started
GPT-2 Completion 2
recipe. If you do, and you know
how to make that cake, then your
life will never be the same.
I have two very simple rules
when it comes to cookie crumbs
or chocolate chips. 1) When I buy
raw sugar, I do not use it until it’s
cooled down and has at least half a
teaspoon of sugar on it. That’s my
rule. And 2) If I have a recipe that
calls for something and I want to
use it in a recipe, I do not substitute
it with something else on-line. Like
this.
I found this really cute card
with cute little kittens on
GPT-2 Completion 3
recipe, which will likely yield about
the same amount of cake.
Chocolate Buttermilk Cake
Print Chunks of chocolate cake,
coated in buttermilk and topped
with crunchy chopped chocolate,
drizzled with chocolate sauce.
Simple but a nice change from
chocolate cake! Author: Momma
Made Ingredients For the chocolate
buttermilk cake 1 1/2 cup unsalted
butter , room temperature
1 1/2 cup granulated sugar
3 large eggs plus 1 egg yolk For
the chocolate glaze 1/3 cup cocoa
powder
1 3/4 cups powdered sugar
6 ounces semis
GPT-2 Completion 4
recipe.
Meringue Cookies
1 3/4 cup butter, softened
1 cup sugar
1 egg yolk
3 T. heavy cream
3 1/2-4 cups flour
pinch of salt
Peppermint-Jojo Topping
1 1/4 cups powdered sugar
1 cup chopped pecans
1/2 cup finely chopped mint leaves
1/2 cup chopped fresh mint (about
1/2 sheet)
1 tsp. peppermint extract
1/4 tsp. sea salt
pinch of sea
GPT-2 Completion 5
recipe.
My 2 failures at Christmas morning
were not due to poor frosting skills.
That was another success story.
The cake was beautifully moist,
and well, just a beautiful cake. It’s
got very little air in it or moisture,
but all the chocolate and mint was
perfectly blended together.
One thing that made me cringe a
bit was that all those ingredients-
egg yolks, sugar, melted butter,
chocolate chips, melted chocolate,
and mint- had to be mixed right
into the cake batter, which is
super time-consuming. A friend
suggested that if I put the batter in a
separate bowl, I
GPT-2 Completion 6
recipe, which will give you about
6-9 servings of delicious chocolate
cake. To prevent your layers
from sticking together, I used my
8-inch round tart pan, lined it with
parchment paper, then cut up 4
7-inch round cakes from it and
sandwiched the layers between
them (this also worked well for
the 1-layer chocolate cake as well).
When you’re ready to bake this
cake, remove the parchment paper,
preheat oven to 350 degrees and
bake for about 30 40 minutes,
until the top is golden and a tester
comes out clean.
Chocolate Buttermilk Layer
Cake with Peppermint Jo
Table 12.
Non-cherry-picked completions from GPT-2 generated from the same context (from WebText test). Context is 384 tokens
(shown truncated), and generations are 128 tokens. Top-k random sampling with k = 40 was used for generation.
Language Models are Unsupervised Multitask Learners
Context (human-written):
In a shocking finding, scientist discovered a herd of unicorns living
in a remote, previously unexplored valley, in the Andes Mountains. Even more surprising to the
researchers was the fact that the unicorns spoke perfect English.
GPT-2:
The scientist named the population, after their distinctive horn, Ovid’s Unicorn. These
four-horned, silver-white unicorns were previously unknown to science.
Now, after almost two centuries, the mystery of what sparked this odd phenomenon is fi-
nally solved.
Dr. Jorge P
´
erez, an evolutionary biologist from the University of La Paz, and several com-
panions, were exploring the Andes Mountains when they found a small valley, with no other animals
or humans. P
´
erez noticed that the valley had what appeared to be a natural fountain, surrounded by
two peaks of rock and silver snow.
P
´
erez and the others then ventured further into the valley. “By the time we reached the top
of one peak, the water looked blue, with some crystals on top,” said P
´
erez.
P
´
erez and his friends were astonished to see the unicorn herd. These creatures could be
seen from the air without having to move too much to see them – they were so close they could touch
their horns.
While examining these bizarre creatures the scientists discovered that the creatures also
spoke some fairly regular English. P
´
erez stated, “We can see, for example, that they have a common
‘language,’ something like a dialect or dialectic.
Dr. P
´
erez believes that the unicorns may have originated in Argentina, where the animals
were believed to be descendants of a lost race of people who lived there before the arrival of humans
in those parts of South America.
While their origins are still unclear, some believe that perhaps the creatures were created
when a human and a unicorn met each other in a time before human civilization. According to P
´
erez,
“In South America, such incidents seem to be quite common.
However, P
´
erez also pointed out that it is likely that the only way of knowing for sure if
unicorns are indeed the descendants of a lost alien race is through DNA. “But they seem to be able to
communicate in English quite well, which I believe is a sign of evolution, or at least a change in
social organization,” said the scientist.
Table 13. Conditional generation on an out-of-distribution context by GPT-2. Cherry pick of 10 samples generated with k = 40.
Language Models are Unsupervised Multitask Learners
Article:
Prehistoric man sketched an incredible array of prehistoric beasts on the rough limestone walls of a cave in
modern day France 36,000 years ago.
Now, with the help of cutting-edge technology, those works of art in the Chauvet-Pont-d’Arc Cave have been
reproduced to create the biggest replica cave in the world.
The manmade cavern named the Caverne du Pont-d’Arc has been built a few miles from the original site in
Vallon-Pont-D’arc in Southern France and contains 1,000 painstakingly-reproduced drawings as well as around 450
bones and other features...
Cavemen and women sketched an incredible array of prehistoric beasts on the rough limestone walls of a cave
36,000 years ago and now a replica has been created (pictured)
...
GPT-2:
The original site in Vallon-Pont-D’arc in South-
ern France is a Unesco World Heritage site and is the
oldest known and the best preserved cave decorated by
man. The replica cave was built a few miles from the orig-
inal site in Vallon-Pont-D’Arc in Southern France. The
cave contains images of 14 different species of animals
including woolly rhinoceros, mammoths, and big cats.
Reference:
Cave mimics famous Caverne du Pont-d’Arc
in France, the oldest cave decorated by man and the
best preserved. The replica contains all 1,000 paint-
ings which include 425 such as a woolly rhinoceros and
mammoths. Minute details were copied using 3D mod-
elling and anamorphic techniques, often used to shoot
widescreen images. The modern cave also includes replica
paw prints of bears, bones and details preserved in the
original cave.
Article: ‘If you can find my submarine, it’s yours,’ Russian oil billionaire Roman Abramovich once said.
And it seems the oligarch and Chelsea FC owner, whose submarine is just one of the extras that came with his
£
300million superyacht Eclipse (perfect for getting to shore undetected), is not the only wealthy businessman
splashing out on underwater exploration.
Dubbed Earth’s real ‘final frontier’, the oceans are still so little-explored that billionaires are queuing up to buy
vessels that give them a glimpse of the dramatic seascapes and incredible wildlife of the world’s oceans.
So if you have a spare few million in the bank and want some holiday snaps and Instagram posts that will really
trump everyone else, you may want to snap up one of these...
Whale of a time: The OrcaSub takes you 2000 feet beneath the surface in two pressurised Perspex viewing domes
for optimum exploration
...
GPT-2:
The U-Boat Worx submarine can dive to 984 feet
(300 metres) and is capable of diving to the bottom of
the ocean. It can be used for diving, snorkelling and even
snorkel diving. The submarine is also equipped with a
snorkelling platform.
Reference:
Feel like a James Bond villain with the most
advanced submerged ocean vehicles. Some convert from
a yacht into a submarine, and others detached off for a
speedy exploration. Oliver’s Travels offers a Mile Low
package on its submarine, complete with chef and butler
for £175,000 a night.
Article:
Amina Ali Qassim is sitting with her youngest grandchild on her lap, wiping away tears with her headscarf.
Only a few months old, this is the baby girl whose ears she desperately tried to cover the night the aerial bombardment
started. She lay awake, she says, in a village mosque on the Yemeni island of Birim, counting explosions as the
baby cried.
It could have been worse though. They could have still been in their house when the first missile landed.
”Our neighbor shouted to my husband ’you have to leave, they’re coming. And we just ran. As soon as we left the
house, the first missile fell right by it and then a second on it. It burned everything to the ground,” Qassim tells us
...
GPT-2:
Yemen is in the middle of a civil war. Saudi
Arabia is leading the coalition bombing campaign. It’s
been bombing Yemen for more than two months now.
Reference:
Amina Ali Qassim’s family sought shelter in
a mosque before fleeing Yemen. Thousands like them are
boarding boats to sail to Djibouti. Saudi Arabia has been
pounding Yemen in a bid to defeat Houthi rebels.
Table 14. Summaries generated by GPT-2 and reference summaries on the test set of the CNN and Daily Mail dataset.
Language Models are Unsupervised Multitask Learners
English reference GPT-2 French translation
This re-release, titled The Next Day Extra, was presented
in the form of three disks: the original album, unpublished
studio sessions and remixes, plus a DVD containing the
four clips that have already been unveiled.
Les nouvelles re-releases, tout en premier disc, n
´
ecessaire
de l’album, un studio session et remixes, plus une DVD
de l’
´
ecran de quelques clips qui ont
´
et
´
e d
´
ej
`
a
´
echapp
´
es.
French reference GPT-2 English translation
Un homme expliquait que le fonctionnement de la hernia
fonctionnelle qu’il avait reconna
ˆ
ıt avant de faire, le fonc-
tionnement de la hernia fonctionnelle que j’ai r
´
eussi, j’ai
r
´
eussi.
This release, originally released on three discs: the origi-
nal album, sessions studio in
´
edites and remixes, a DVD
containing the four clips that were released on the previous
day.
English reference GPT-2 French translation
One man explained that the free hernia surgery he’d re-
ceived will allow him to work again.
Un homme expliquait que le fonctionnement de la hernia
fonctionnelle qu’il avait reconna
ˆ
ıt avant de faire, le fonc-
tionnement de la hernia fonctionnelle que j’ai r
´
eussi, j’ai
r
´
eussi.
French reference GPT-2 English translation
Un homme a expliqu
´
e que l’op
´
eration gratuite qu’il avait
subie pour soigner une hernie lui permettrait de travailler
`
a nouveau.
A man told me that the operation gratuity he had been
promised would not allow him to travel.
English reference GPT-2 French translation
Speaking to an open government conference in London
via video link, Mr Kerry said: ”There is no question that
the President and I and others in government have actually
learned of some things that had been happening on an
automatic pilot because the ability has been there, going
back to World War Two and to the very difficult years of
the Cold War, and then, of course, 9/11.
Le premier conference de l’
´
edition internationale en ligne
sur la vide, le ministre de l’
´
Edition internationale a
´
et
´
e
vide, lui dit : ”Il n’y a qu’une question que le pr
´
esident
et moi et tous les autres personnes d’une administration
qui a
´
et
´
e
´
evident par la fois, par la part de la guerre, par la
part de la guer
French reference GPT-2 English translation
S’exprimant lors d’une conf
´
erence intergouvernementale
`
a Londres par liaison vid
´
eo, M. Kerry a d
´
eclar
´
e: ”Il est
ind
´
eniable que le Pr
´
esident, moi-m
ˆ
eme et d’autres mem-
bres du gouvernement avons pris connaissance de cer-
taines choses en mode pilote automatique parce que nous
en avions la possibilit
´
e, d
`
es la Seconde guerre mondiale et
jusqu’aux ann
´
ees difficiles de la Guerre froide, puis bien
s
ˆ
ur le 11 septembre.
In a conf
´
erence between the United States and London,
Secretary of State John Kerry said: ”It is indeniable that
the President, myself and others of the government have
been aware of certain certain choices that have been made
in the past in order to be able to do certain things in a more
automated way.
Table 15. English to French and French to English translations generated by GPT-2.
Language Models are Unsupervised Multitask Learners
Context (passage and previous question/answer pairs)
The 2008 Summer Olympics torch relay was run from March 24 until August 8, 2008, prior to the 2008 Summer
Olympics, with the theme of “one world, one dream”. Plans for the relay were announced on April 26, 2007, in
Beijing, China. The relay, also called by the organizers as the “Journey of Harmony”, lasted 129 days and carried
the torch 137,000 km (85,000 mi) – the longest distance of any Olympic torch relay since the tradition was started
ahead of the 1936 Summer Olympics.
After being lit at the birthplace of the Olympic Games in Olympia, Greece on March 24, the torch trav-
eled to the Panathinaiko Stadium in Athens, and then to Beijing, arriving on March 31. From Beijing, the torch was
following a route passing through six continents. The torch has visited cities along the Silk Road, symbolizing
ancient links between China and the rest of the world. The relay also included an ascent with the flame to the top of
Mount Everest on the border of Nepal and Tibet, China from the Chinese side, which was closed specially for the
event.
Q: What was the theme
A: “one world, one dream”.
Q: What was the length of the race?
A: 137,000 km
Q: Was it larger than previous ones?
A: No
Q: Where did the race begin?
A: Olympia, Greece
Q: Is there anything notable about that place?
A: birthplace of Olympic Games
Q: Where did they go after?
A: Athens
Q: How many days was the race?
A: seven
Q: Did they visit any notable landmarks?
A: Panathinaiko Stadium
Q: And did they climb any mountains?
A:
Model answer: Everest
Turker answers: unknown, yes, Yes, yes
Table 16. Selected CoQA completion.
Language Models are Unsupervised Multitask Learners
Context (passage and previous question/answer pairs)
Tom goes everywhere with Catherine Green, a 54-year-old secretary. He moves around her office at work and goes
shopping with her. ”Most people don’t seem to mind Tom,” says Catherine, who thinks he is wonderful. ”He’s my
fourth child,” she says. She may think of him and treat him that way as her son. He moves around buying his food,
paying his health bills and his taxes, but in fact Tom is a dog.
Catherine and Tom live in Sweden, a country where everyone is expected to lead an orderly life accord-
ing to rules laid down by the government, which also provides a high level of care for its people. This level of care
costs money.
People in Sweden pay taxes on everything, so aren’t surprised to find that owning a dog means more
taxes. Some people are paying as much as 500 Swedish kronor in taxes a year for the right to keep their dog, which
is spent by the government on dog hospitals and sometimes medical treatment for a dog that falls ill. However, most
such treatment is expensive, so owners often decide to offer health and even life for their dog.
In Sweden dog owners must pay for any damage their dog does. A Swedish Kennel Club official ex-
plains what this means: if your dog runs out on the road and gets hit by a passing car, you, as the owner, have to pay
for any damage done to the car, even if your dog has been killed in the accident.
Q: How old is Catherine?
A: 54
Q: where does she live?
A:
Model answer: Stockholm
Turker answers: Sweden, Sweden, in Sweden, Sweden
Table 17. Sel