Multitask Prompted Training Enables Zero-Shot Task Generalization
Authors: Victor Sanh
Albert Webson
Colin Raffel
Stephen Bach
Lintang Sutawika
Zaid Alyafeai
Antoine Chaffin
Arnaud Stiegler
Arun Raja
Manan Dey
M Saiful Bari
Canwen Xu
Urmish Thakker
Shanya Sharma Sharma
Eliza Szczechla
Taewoon Kim
Gunjan Chhablani
Nihal Nayak
Debajyoti Datta
Jonathan Chang
Mike Tian-Jian Jiang
Han Wang
Matteo Manica
Sheng Shen
Zheng Xin Yong
Harshit Pandey
Rachel Bawden
Thomas Wang
Trishala Neeraj
Jos Rozen
Abheesht Sharma
Andrea Santilli
Thibault Fevry
Jason Alan Fries
Ryan Teehan
Teven Le Scao
Stella Biderman
Leo Gao
Thomas Wolf
Alexander M Rush
Venue: ICLR 2022 Spotlight (2022)

Abstract: Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020). It has been hypothesized that this is a consequence of implicit multitask learning in language models’ pretraining (Radford et al., 2019). Can zero-shot generalization instead be directly induced by explicit multitask learning? To test this question at scale, we develop a system for easily mapping any natural language tasks into a human-readable prompted form. We convert a large set of supervised datasets, each with multiple prompts with diverse wording. These prompted datasets allow for benchmarking the ability of a model to perform completely unseen tasks specified in natural language. We fine-tune a pretrained encoder-decoder model (Raffel et al., 2020; Lester et al., 2021) on this multitask mixture covering a wide variety of tasks. The model attains strong zero-shot performance on several datasets, often outperforming models 16× its size. Further, our model attains strong performance on a subset of tasks from the BIG-Bench benchmark, outperforming models 6× its size. All trained models are available at https://github.com/bigscience-workshop/t-zero, and all prompts are available at https://github.com/bigscience-workshop/promptsource.

Published as a conference paper at ICLR 2022
MULTITASK PROMPTED TRAINING ENABLES ZERO-SHOT TASK GENERALIZATION
Victor Sanh* Hugging Face
Albert Webson'
Colin Raffel Hugging Face
Stephen H. Bach" Brown & Snorkel AI
Brown University
Lintang Sutawika
Zaid Alyafcai
Antoine Chaffin
A Arnaud Stiegler
Teven Le Scao Hugging Face
BigScience
KFUPM
IRISA & IMATAG
Hyperscience
Arun Raja
Manan Dey
Saiful Bari NTU, Singapore
Canwen Xu UCSD & Hugging Face
Urmish Thakker
FR. Singapore
SAP
SambaNova Systems
Shanya Sharma
Eliza Szezechla
Taewoon Kim
Gunjan Chhablani
Nihal Nayak
Walmart Labs
BigScience
VU Amsterdam
BigScience
Brown University
Debajyoti Datta University of Virginia
Jonathan Chang ASUS
Mike Tian-Jian Jiang
Han Wang NYU
Matteo Manica
ZEALS, Japan
IBM Research
Sheng Shen
Zheng-Xin Yong
Harshit Pandey
Michael McKenna
Rachel Bawden
Berkeley
Brown University
BigScience
Parity
Inria, France
Thomas Wang Inria, France
Trishala Neeraj
Jos Rozen
Abheesht Sharma
Andrea Santilli University of Rome
BigSeience
Naver Labs Europe
BITS Pilani, India
Thibault Fevry BigScience
Jason Alan Fries
Ryan Teehan
Tali Bers
Stanford & Snorkel AI
Charles River Analytics
Brown University
Stella Biderman Booz Allen & EleutherAl
Leo Gao
Thomas Wolf
Alexander M. Rush Hugging Face
EleutherAl
Hugging Face
ABSTRACT
Large language models have recently been shown to attain reasonable zero-shot generalization diverse set of tasks (Brown al., 2020). It has been hypothe- sized that this a consequence of implicit multitask learning in language models' pretraining (Radford al., 2019). Can zero-shot generalization instead be directly induced by explicit multitask learning? To test this question at scale, we develop system for easily mapping any natural language tasks into prompted form. We convert a large set of supervised datasets, each with multiple prompts with diverse wording. These prompted datasets allow for benchmarking the ability of a model to perform completely unseen tasks. We fine-tune a pre- trained incoder-decoder model (Raffel et al., 2020; Lester al., 2021) on this mul- titask mixture covering a wide variety of tasks. The model attains strong zero-shot performance on several standard datasets, often outperforming models up to 16x its size. Further, approach attains strong performance the BIG-bench benchmark, outperforming models up to 6x its size. All trained models available https://gitubb.co//iggeee prompts are available at
readaalle
subset of tasks from
workshop/t-zero, and all
INTRODUCTION
Recent work has shown that large language models exhibit the ability to perform reasonable zero- shot generalization to new tasks (Brown al., 2020; Kim et al., 2021). Despite being trained only language modeling objectives, these models can perform relatively well at new tasks that they have not been explicitly trained to perform, for instance answering question on passage or performing summarization. An influential hypothesis is that large language models generalize to new tasks as
Equull contribution. Full list of individual contributions detailed in Appendix A.

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Summarization
The picture eppeared on the wall PoundIand store on Whynark Avenue [...] J How would you rephrase that few words?
Sentiment Analysts
Graffiti artist Banksy is believed be behind [...]
We cane here on Saturday night and luckily it waan t as packed as I thought it would [...] to 5, would give this
Review:
scale of
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Question Answering
T0
know that the answer to "What team did Panthers defeat?" finished the regular season [...]". Can you tell what it is?
Panthers
Arizone Cardinels
Multi-task training
Zero-shot generalization
Natural Language Inference
Suppose The banker contacted the professors and the athlete". Can infer that 'The banker contacted the professors"?
Yes
Our model and prompt format. TO is encoder-decoder model that consumes textual inputs and produces target responses. It is trained on a multitask mixture of NLP datasets parti- tioned into different tasks. Each dataset is associated with multiple prompt templates that used to format example instances to input and target pairs. Italics indicate the inserted fields from the raw example data. After training on a diverse mixture of tasks (top), our model is evaluated on zero-shot generalization to tasks that are not seen during training (bottom).
Figure 1:
result of an implicit process of multitask learning (Radford et al., 2019). As a byproduct of learning to predict the next word, a language model is forced to learn from mixture of implicit tasks included in their pretraining corpus. For example, by training on generic text from a web forum, model might implicitly learn the format and structure of question answering. This gives large language models the ability to generalize to unseen tasks presented with natural language prompts, going beyond prior multitask studies generalization to unseen datasets (Khashabi et al., 2020a; Ye et al., 2021). However, this ability requires a sufficiently large model and is sensitive to the wording of its prompts (Perez al., 2021; Zhao et al., 2021; Reynolds and McDonell, 2021).
Further, it is an open question how implicit this multitask learning really is. Given the scale of recent language models' pretraining corpora, it is reasonable to expect that some common natural language processing (NLP) tasks would appear in an explicit form in their pretraining corpora, thereby directly training the models on those tasks. For example, there are many websites that simply contain lists of trivia questions and answers, which are precisely supervised training data for the task of closed- book question answering (Roberts et al., 2020). We hypothesize that such multitask supervision in pretraining plays large role in zero-shot generalization.
In this paper, we focus on explicitly training language models in a supervised and massively multi- task fashion. Our approach uses a training mixture consisting of large set of different tasks specified in natural language prompts. Our goal is to induce a model better generalize to unseen tasks with- out requiring massive scale, well as being more robust to the wording choices of the prompts. To convert a large set natural language tasks into prompted form, we use a simple templating language for structured datasets. We develop an interface for prompt collection from public contrib- utors that facilitated the collection of a large multitask mixture with multiple prompts per dataset (Bach al., 2022). We then train variant of the T5 encoder-decoder model (Raffel et al., 2020; Lester et al., 2021) on a subset of the tasks (each with multiple datasets) and then evaluate tasks and prompts that the model was not trained on.
Our experiments study two questions. First, does multitask prompted training improve generaliza- tion to unseen tasks? Second, does training on wider range of prompts improve robustness to prompt wording? For the first question, we find that multitask training enables zero-shot task gen- cralization by showing that our model matches or exceeds the performance of GPT-3 (Brown al., 2020) on 9 out of 11 held-out datasets, despite being about 16x smaller. We also show that the model improves over large baseline language model on 13 out of 14 tasks in the BIG-bench benchmark
Forr example, http://wwww..aireeeeeeccc/mmttts guestions-answersl,, and http:://ppnnd....o/////////////p for-knsss/
http://www...rymmmmmmcmmtttttttt
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(BIG-bench collaboration, 2021). For the second question, find that training more prompts per dataset consistently improves the median and decreases the variability of performance on held-out tasks. Training on prompts from wider range of datasets also generally improves the median but does not consistently decrease the variability.
2 RELATED WORK
In this work, we distinguish implicit multitask learning language model pretraining from explicir multitask learning (Caruana, 1997), the technique for mixing multiple tasks into single supervised training process. Models trained with multitask learning have long been shown to have improved performance in NLP (Collobert and Weston, 2008). Since different tasks have different outputs, ap- plying multitask learning requires a shared format, and various have been used (Hashimoto et al., 2016; McCann et al., 2018). Several multitask works also explore few-shot and zero-shot general- ization to new datasets with large pretrained models (e.g., Vu et al., 2020; Ye et al., 2021).
Natural language prompting is the method of reformatting NLP tasks in the format of natural language response to natural language input. The development of text-to-text pretrained models such T5 (Raffel al., 2020) makes prompts a particularly useful method for multitask learning. For example, Khashabi et al. (2020a) reformat 20
datasets into a single prompt context: :
while later work such as Zhong et al. (2021) and Wang et al. (2021) cast range of datasets into a single boolean QA prompt or a single NLI prompt, respectively. Although effective, these single-prompt methods typically do not generalize to new prompts new tasks inexpressible in their fixed format.
of question:
(A)
(B)... (C)
More generally, Schick and Schütze (2021) and Brown et al. (2020) popularized using prompts as generic method for all NLP tasks. Mishra et al. (2021) further extend this approach to multitask setup, training on prompts for 61 narrowly defined tasks (e.g., question generation, incorrect answer generation) adapted from 9 datasets' crowdsourcing instructions, whereas train and measure generalization across 62 datasets and 12 tasks as traditionally defined in the NLP literature ($3). Additionally, their prompts include labeled examples in addition instructions, whereas focus on zero-shot generalization. Lastly, concurrent work by Wei et al. (2021) shares a similar research question with us, although differ in several substantive regards, e.g., prompt diversity, model scale, and held-out-task scheme. We discuss our differences in detail in Section 7.
Finally, in explaining the success of prompts, the leading hypothesis is that models learn to under- stand the prompts as task instructions which help them generalize to unseen tasks (Wei et al., 2021; Mishra et al.. 2021; Schick and Schütze, 2021; Brown al.. 2020). However, the extent to which this success depends on the semantic meaningfulness of the prompts has been challenged (Webson and Pavlick, 2021; Logan et al., 2021). Thus, in this work, we remain agnostic as to why prompts support generalization. We only claim that prompts serve as a natural format for multitask training which empirically supports generalization to unseen tasks.
MEASURING GENERALIZATION TO UNSEEN TASKS
We begin by assuming an underlying partition of NLP datasets into tasks. We use the term "task" to refer to a general NLP ability that is tested by a group of specific datasets. To evaluate zero-shot generalization to new tasks, we train on a subset of tasks and evaluate on held-out group of tasks.
Unfortunately, NLP task categorization is fuzzy, particularly if one tries to isolate a unique skill. For example, many datasets evaluate commonsense knowledge, and some multitask works (e.g.. Brown et al., 2020; Wei et al., 2021) define commonsense as a standalone task. However, commonsense datasets differ vastly, ranging from innate knowledge and grade-school science to DIY instructions, US cultural norms, and graduate-level theorems (see Appendix E. for detailed discussion).
Noting that grouping by task is an imperfect heuristic, we err on the side of organizing our task taxonomy according to the task format opposed to required skill based conventions in the literature (Khashabi et al., 2020b; Vu et al., 2020; Ye et al., 2021). We collect all datasets from these papers and exclude those that are not in English (which also excludes programming languages and structured annotations such as parse trees) or they require special domain knowledge (c.g., biomedicine). This yields 12 tasks and 62 datasets with publicly contributed prompts in training
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Mutiple-Coiice QA
Closed-Bood QA
Structure- Text
Sentence Completion
BlG-BBnchh
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COPA
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PAWS
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TREC
Word Sense Disambiguation
Vitamin C
Quoref
Winowhy
ROPES
WiC
DuoRC
Figure 2: TO datasets and task taxonomy. (TO+ and TO++ are trained on additional datasets. See Table 5 for the full list.) Color represents the level of supervision. Yellow datasets are in the training mixture. Green datasets are held out and represent tasks that were not seen during training. Hotpot QA is recast closed-book QA due long input length.
and evaluation mixtures (Figure 2) of writing. All experiments use datasets in the Hugging Face datasets library (Lhoest et al., 2021).
To test zero-shot generalization, we hold out all constituent datasets of four tasks: natural language inference (NLI), coreference resolution, sentence completion, and word sense disambiguation. We choose NLI as held-out task because humans also zero-shot generalize NLI Most humans are never explicitly trained to classify whether a premise sentence entails or contradicts hypothesis sentence, yet they find it intuitive perform this task without training (Williams al., 2020). For the same reason, we also hold out coreference resolution and word sense disambiguation. We further hold out sentence completion because it task possibly E.2 discusses this in detail). Additionally, we do not train our main model on any datasets that Brown al. (2020) used for evaluation, so that our main results will be a fair zero-shot comparison. We also verify that data for those tasks is not leaked through the pretraining corpus (Appendix F).
unseen task:
similar NLI (Appendix
Lastly, we further evaluate on subset of the datasets from BIG-bench, which is recent community- driven benchmark to create diverse collection of difficult tasks to test the abilities of large language models. The subset of BIG-bench comprise a language-orient oriented selection of tasks for which the BIG- bench maintainers have prepared preliminary results and which constitute text that is in-vocabulary for the tokenizer (i.e. only contain English-language text without emojis or other special charac- ters). All tasks from BIG-bench are novel tasks that were unseen in our training.
A UNIFIED PROMPT FORMAT
All datasets are given to model in natural language prompted form enable zero-shot exper- imentation. To facilitate writing a large collection of prompts, we develop a templating language and an application that make it easy to convert diverse datasets into prompts. We define prompt as consisting of an input template and a target template, along with collection of associated meta- data. The templates are functions mapping a data example into natural language for the input and target sequences. Practically. the templates allow the user to mix arbitrary text with the data fields, metadata, and other code for rendering and formatting raw fields. For example, in the case of an NLI dataset, the example would include fields for Pr Premise, Hypothesis, Label. An input tem- plate would be If [Premise] is true, is it also true that (Hypothesis) whereas target template be defined with the label choices [Choices [label]] Here Choice ces is prompt-specific metadata that consists of the options yes, maybe, no correspond- ing to label being entailment (0), neutral (1) contradiction (2). Other metadata documents
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QQP (Paraphrase)
XSum (Summary)
Queutios1
Nos La our traffic controlles?
Decument
The picture appeared sall
"oundlard
Quection)
Nox do you becare air traffis controliar?
Suauary
Graffitt artiet Sentay in belsoved to
(vvettion)) (Qacstiord) Pick Theae queatione daplicates not deplscates.
recsived the questiona "dvestlenn)" and (Qunerson2) Are thay suplicabes?
(Gocoment) Hos eould you reshrase that Le foo words?
First, pleose reed the ortacle: (Ducsweec)
Roe, can you write re extrenely short abstract for 1t?
#boticed 11
(hstoww/ labelly
[Saimery)
Figure 3: Prompt templates from the P3 prompt collection. Each dataset has multiple prompt tem- plates consisting of an input and a target template. These use the fields of the raw data examples as well as template metadata, e.g., the left paraphrasing identification prompts use Choices, template- level list variable [' ('Not duplicates ', 'Duplicates J. These templates are materialized to produce the prompted instance shown in Figure 1. The complete set of prompt templates used in TO is given in Appendix H.
additional properties, such as evaluation metric. Each data example is materialized with many different prompt templates as shown in Figure 3.
To develop prompts, we built interface for interactively writing prompts on datasets. We put out an open call in the research community for users to contribute prompts. 36 contributors affiliated with 24 institutions in 8 countries participated. Since
goal was train model to be robust to prompt format, and since the question of what makes a prompt effective remains unresolved ( (Webson and Pavlick, 2021; Logan et al., 2021; Zhao et al., 2021), encouraged contributors be open in their style and create a diverse set of prompts. The main annotation guideline was that prompts needed to be grammatical and understandable by fluent English speaker with no prior experience of the tasks. Additionally, prompts that required explicit counting or numerical indexing were removed in favor of natural language variants. For example, instead of predicting indices of a span extracting answers from passage, the model is expected to copy the span's 's text instead. With these minimal constraints, prompt writers were encouraged to use both formal and creative prompts and various orderings of the data.
Most of the prompts correspond directly to a version of the original proposed task, although we also allow prompts that permuted the original task (for instance, generating a document from its sum- mary). Such non-original-tast prompts are included in our training mixtures for improved diversity, but they are not reported in evaluation since they deviate from the metrics and baselines reported by the original datasets.
The details of the prompting language and tool are given in Appendix D and Bach al. (2022), and the prompts themselves are given in Appendix H. We collected prompts for English datasets, excluding ones that included potentially harmful content or not-natural language such program- ming languages. We refer to this collection as the Public Pool of Prompts (P3). As of writing, P3 contains 2073 prompts for 177 datasets 11..7 prompts per dataset average). Prompts used in periments are all sourced from P3 except for BIG-bench, the prompts of which are provided by its maintainers.
EXPERIMENTAL SETUP
Model At high level, we fine-tune a pretrained model on our multi-task training mixture of natural language prompted datasets. Our model uses an encoder-decoder architecture with input text fed to the encoder and target text produced by the decoder. The model is trained to autoregressively generate the target through standard maximum likelihood training. Unlike decoder-only language models such GPT-3, it is never trained to generate the input.
All models we trained are based on T5, a Transformer-bart based encoder-decoder language model pre- trained with masked language modeling-style objective on IT tokens from C4 (Raffel et al., 2020). Since T5's pretraining objective is generating tokens and only tokens that have been removed from the input text, it is different from the natural text generation format of prompted datasets. Therefore, we use Lester et al. (2021)'s LM-adapted T5 model (referred to as T5+LM), produced by training T5 100B additional tokens from C4 standard language modeling objective.
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Figure 4: Results for TO task generalization experiments compared to GPT-3 (Brown et al., 2020).
Each dot is the performance of one evaluation prompt. The baseline T5+LM model is the same as TO except without multitask prompted training. GPT-3 only reports single prompt for each dataset.
Training Our main model, TO, is trained on the multitask mixture detailed in Section 3 and Table 5. Meanwhile, TO+ is the same model with identical hyperparameters except trained on a mixture that adds GPT-3's evaluation datasets. Lastly, T0++ further adds SuperGLUE (Wang et al., 2019a) to the training mixture (except RTE and CB), which leaves NLI and the BIG-bench tasks as the only held-out tasks.
The above TO variants are all initialized from the 11B parameters version of T5+LM. To study the effect of scaling and to aid researchers with less resources, we also train TO (3B), which has the same training mixture as TO but is initialized from the 3B parameters version of T5+LM (results reported in Appendix G).
We perform checkpoint selection by choosing the checkpoint that yields the highest score on the validation splits of our training datasets. This still satisfies the true zero-shot (Perez al., 2021) setting as we do not use any examples from any of the held-out tasks to select the best checkpoint.
We assemble our multitask training mixture by combining and shuffling all examples from all train- ing datasets. This is equivalent to sampling from each dataset in proportion to the number of ex- amples in the dataset. However, the number of examples in each of our training datasets varies by two orders of magnitude. We therefore follow the strategy used in Raffel et al. (2020) and treat any dataset with over 500'000 examples as having 500'000 / num_templ a tes examples for the purposes of sampling, where num.template is the number of templates created for the dataset.
We truncate input and target sequences to 1024 and 256 tokens, respectively. Following Raffel et al. (2020), we use packing to combine multiple training examples into a single sequence to reach the maximum sequence length. We use a batch size of 1024 sequences (corresponding to 2n total in- put tokens per batch) and the Adafactor optimizer (Shazeer and Stern, 2018). Following standard practice for fine-tuning T5, we use a learning rate of 1e-3 and a dropout rate of 0.
Evaluation We evaluate zero-shot generalization 11 datasets in 4 held-out traditional NLP tasks: natural language inference, coreference, word sense disambiguation, and sentence completion, as well as 14 novel tasks from BIG-bench ($3). Unless specified otherwise, we report performance on the validation splits. All reported datasets use accuracy as their metric.
For tasks that involve choosing the correct completion from several options (e.g. multiple choice question answering). we follow Brown et al. (2020) and use rank classification to evaluate our model: we compute the log- likelihoooo of each of the target options under the fine-tuned model and
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Code Description
Conceptual
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Figure 5: Results for a subset of BIG-bench which has available baselines. The baseline models are Transformer-based language models provided by BIG-bench maintainers, who also provide one prompt per dataset. TO, T0+ and T0++ are identical except for increasing the number of training datasets ($5). BIG-bench Tasks are all zero-shot for all the reported models.
select the option with the highest log-likelihood as the prediction. For simplicity, we do not apply length normalization to the log- ligeiiiessoods of the target options.
We do not perform prompt selection by comparing the performance of different prompts the vali- dation split; Perez et al. (2021) highlights how such a strategy leaks information from the evaluation splits, which makes the evaluation not "true" zero-shot. For given dataset, report the median performance across all prompts for this dataset along with their interquartile range (Q3 - Q1) to measure the model's robustness the wording of the prompts.
RESULTS
6.1
GENERALIZATION TO UNSEEN TASKS
Our first research question is whether multitask prompted training improves generalization to unseen tasks. In Figure 4, we compare TO against our T5+LM baseline on four held-out tasks. Our approach leads to significant gains over our baseline on all datasets, demonstrating the benefits of multitask prompted training over only language modeling training with an identical model and prompts.
Next, we compare TO to the zero-shot performance of the largest language models available as of writing, i.e., various GPT-3 models up to 175B parameters. Note that Brown et al. (2020) report per- formance on a single prompt, whereas we report the median and interquartile range of performance across all prompts P3 without cherry picking. We find that TO matches or exceeds the performance of all GPT-3 models on 9 out of 11 held-out datasets. Notably, neither TO nor GPT-3 is trained on natural language inference, yet TO outperforms GPT-3 on all NLI datasets, even though our T5+LM baseline does not. The same is true for most datasets of other held-out tasks. The two exceptions Winogrande and HellaSwag, which we discuss in Section
To evaluate models TO++ on a subset of BIG-bench (BIG-bench collaboration, 2021). Tasks from BIG-bench cover a variety of novel skills not included in training tasks, such as deducing the order of a sequence of objects, solving logic grid puzzles, and telling apart true statements from common misconcep- tions. The maintainers of BIG-bench provide prompt for each dataset, with which we compare our models to a series of preliminary diagnostic baseline models trained by Google and evaluated by the BIG-bench maintainers. These models are decoder-only Transformer language models trained on standard language modeling objective with varying model size. We find that at least one of the
unseen tasks, assess the zero-shot performance of TO, TO+, and
?Our experiments in Section 6.2 lead us to believe that this performance corresponds to the best prompt found after manual tuning according validation set performance.
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Figure 6: Effect of more prompts per dataset. Zero-shot performance of TO and T5+LM when in- creasing number of training prompts per dataset. Each dot is the performance of one evaluation prompt. The main TO model (p 8.03) includes non-original-tash prompts (see Section 3). Adding more training prompts consistently leads to higher median performance and generally lower in- terquartile range for unseen tasks.
TO variants outperform all baseline models on all tasks except for StrategyQA (Figure 5). In most cases, the performance of our models improves as the number of training datasets increases (i.e., TO++ outperforms TO+ which outperforms TO).
6.2 PROMPT ROBUSTNESS
Our second research question whether training on wider range of prompts improves robustness to the wording of the prompts. We conduct two ablation experiments on the effects of the average number prompts per dataset (p) and the number of datasets (d) used during training.
Effect of More Prompts per Dataset In this analysis, we fix and compare TO to models with a varying number of prompts per dataset. TO was trained some prompts that do not map onto the dataset's original task, for example "given an answer, generate a plausible question". Including these prompts results in p being 8.03 average (which corresponds to main TO model). We compare TO to models where p = 1 (one randomly chosen original-tast prompt per dataset), p = 5.7 on average (all original-tasks prompts for all datasets), and p (corresponding to T5+LM without any prompted training). We train all models with the same hyperparameters and the same number of steps. Figure shows that, even with just one prompt per dataset, performance unseen tasks can improve substantially over the non- promppet baseline, although the spread (interquartil range between Q1 and Q3) does not consistently improve with p 1. Mcanwhile, further increasing p from to an average of 5.7 does yield additional improvement in both median (increases for 8/11 datasets) and spread (decreases for 7/11 datasets). This reinforces our hypothesis that training on more prompts per dataset leads to better and more robust generalization to unseen tasks. Finally, we find that TO's inclusion all prompts (including those that do not correspond to the dataset's original task) further improves the median (increases for 9/11 I datasets) and spread (decreases for 8/11 datasets), showing that training on non-original-tan task prompts can also be beneficial.
Effect of Prompts from More Datasets In this experiment, we fix p= all available prompts and increase d from 39 to 49 55 (TO, TO+, T0++, respectively. See Section 5 for details.) Figure shows that the median performance of all 5 held-out datasets increases as d increases from 39 to 49. However, the spread only decreases for I out of 5 datasets. For some datasets (e.g., ANL.), this is an
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0
30
30
30
TO (d 39)
10+ (d 49)
T0++ (d- 55)
Figure 7: Effect of prompts from more datasets. Zero-shot performance of three models with varying number of datasets (TO, TO+, T0++). Adding more datasets consistently leads to higher median performance but does not always reduce interquartile range for unseen tasks.
artifact of the fact that some prompts always perform poorly, so that when other prompts improve, the spread is stretched larger. For other datasets (e.g.. CB), however, the spread does decrease with T0+. As d increases from 49 to 55, the median performance of all datasets again increases, but the spread only decreases for out of 5 datasets. Although further investigation is needed, it appears that increasing d does not consistently make the model more robust to the wording of prompts.
Comparing TO and GPT-3's robustness Because Brown al. (2020) only report one prompt per dataset with no standard deviation, we evaluate GPT-3 via OpenAl's API on RTE using the same 10 prompts prompts. One of these templates is identical to Brown et al. (2020, 59)'s reported prompt, which scores an accuracy of 58.8%, lower than the 63.5% reported in Brown al. (2020). All other prompts, however, yield roughly random gueossin performance with median accuracy 52.96% and interquartile range- 1.28%. These results suggest that TO could be more robust prompt formulation than GPT-3.
evaluate TO in order estimate GPT-3 robustness' to different wording of
7 COMPARISON TO FLAN
Concurrent to work, Wei al. (2021) proposes FLAN, which shares largely the same method of enabling zero-shot generalization through multitask prompted training. With a mixture of datasets similar ours, they train multiple decoder-only language models, each with single held-out task (cf. we focus on training one model with multiple held-out tasks in order to evaluate the model's ability to generalize to diverse tasks.) Compared to FLAN, TO's zero-shot performance is better on CB and RTE, similar on Story Cloze and COPA, and worse on Winogrande and ANLI. TO++ outperforms FLAN on CB, RTE, and COPA and matches FLAN's performance on Winogrande and ANLI Notably, TO and T0++ attain this performance despite being over 10x smaller than FLAN (137B vs. 1 IIB parameters). Appendix B discusses our differences in details.
CONCLUSION
We demonstrate that multitask prompted training can enable strong zero-shot generalization abilities in language models. This approach provides an effective alternative to unsupervised language model pretraining, often enabling our TO model to outperform models many times its size. We also perform ablation studies demonstrating the importance of including many diverse prompts and the impact of increasing the number of datasets in each task. To enable future work on improving zero-shot generalization, we release all models trained in this paper in addition to the collection prompts created and our prompt annotation tool.
"https:/beta..penna www.oopennocooo.e com/ We use the "base GPT-3 model" davinci. Although OpenAl does not disclose which one of their commercially available models correspond to which models reported in Brown et al. (2020), Gao et al. (2021) estimate that davi nei corresponds to the 175B model.
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ACKNOWLEDGEMENTS
This work was granted access to the HPC resources of Institut du développement et des ressources en informatique scientifique (IDRIS) du Centre national de la recherche scientifique (CNRS) under the allocation 2021 10211001110 made by Grand équipement national de calcul intensif (GENCI). In particular, all the evaluations and data processing ran on the Jean-Zay cluster of IDRIS, and we want to thank the IDRIS team for responsive support throughout the project, in particular Rémi Lacroix. We are grateful for the TPU Research Cloud program which generously provided TPU credits to Hugging Face. Those credits were used to train all the models from this paper.
This work was partly funded by Rachel Bawden and Benoît Sagot's chairs in the PRAIRIE institute funded by the French national agency ANR as part of the "Investissements d' avvnnr"" programme under the reference ANR- 11R-19-P31A-0001 Disclosure: Stephen Bach contributed to this work as an advisor to Snorkel AL
We thank Yacine Jemite, Sasha Luccioni, Aurélie Névéol and Huu Nguyen for advising on strategies to deal with datasets containing potentially harmful content. Guy Gur-Ari and Ethan Dyer provided assistance and preliminary results on BIG-hench evaluation. We thank Ruiqi Zhong for early dis- cussions this project.
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A CONTRIBUTIONS AND PROJECT STRUCTURE
This research was conducted under the BigScience project for open research,' 4 year-long initiative targeting the study of large models and datasets. The goal of the project is to research language models in public environment outside large technology companies. The project has 600 researchers from 50 countries and more than 250 institutions. The BigScience project was initiated by Thomas Wolf at Hugging Face, and this collaboration would have been possible without his effort. This research was the focus of the BigScience Prompt Engineering working group, which focused on the role of prompting in large language model training.
This project was led by the joint first-authors of this work. Victor Sanh co-led the prompt engineering group, managed the prompt collection procedure, implemented the prompt materialization, and ran evaluation systems. Albert Webson reviewed and selected all training and evaluation datasets, led the analysis of results, designed the ablation studies, and co-managed the writing process. Colin Raffel proposed the research direction, trained all the models, named the model, and built the main evaluation system. Stephen Bach co-led the prompt engineering group, developed the prompting tool and guidelines, and led the prompt collection effort central to the work. Additionally, Alexander Rush helped develop the prompt templating language and tool, and co- co-managed paper writing.
Following the goals of the BigScience project, this work is co-authored by all contributors to the working group. We define this contribution as having contributed at least accepted prompted sorted based on code contributions to
datasets to the project. Lacking better metric, authors the project. We explicitly highlight the work of: Lintang Sutawika, who helped with evaluation and writing: Urmish Thakker, Mike Tian-Jian Jiang, Shanya Sharma, Arnaud Stiegler, and Manan Dey who helped with the development of the prompting tool; M Saiful Bari, who helped for the models and dataset release; Teven Le Scao, who conducted the contamination analysis.
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B ADDITIONAL DISCUSSION OF DIFFERENCES WITH FLAN
Both TO and FLAN underperform GPT-3 on Winogrande and HellaSwag (Sakaguchi et al., 2019; Zellers et al., 2019), for which Wei et al. (2021) conjecture that for tasks such as coreference resolu- tion that can be formatted as finishing an incomplete sentence, adding task instructions to prompts is "largely redundant". Following this conjecture, we reevaluate these two datasets without instruc- tions as done by Wei et al. (2021) and Brown et al. (2020) and find that it improves performance on HellaSwag from median of 33.65% to 57.93%, matching the performance of FLAN. For Wino- grande, however, using FLAN's prompt without instructions does not make a substantial difference (accuracy 62%%.
Surprisingly, Wei et al. (2021) perform an ablation with a model of comparable size (8B parameters) to TO (1IB parameters) and find that that performance on held-out tasks decreases after multitask prompted training. whereas we find that multitask prompted training improves the performance of models at least as small as 3B parameters (Figure 8). We identify two key differences between the models that could explain this discrepancy: First, we use an encoder- pretrained with different objective (masked language modeling) before being trained as a standard language model and finally fine-tuned on the multitask mixture. We note that masked language modeling has repeatedly been shown to be dramatically more effective pre-training strategy (Raffel et al., 2020; Baevski et al., 2019; Devlin et al., 2019).
model that was
Second, our prompts are qualitatively more diverse in terms of their length and creativity (54). For example, consider one of our prompts for Quora Question Pairs (paraphrasing identification): I'm m an administrator on the website Quora. There are two posts, one that asks "question1" " and another that asks "question2". I can merge questions if they are asking the same thing. Can merge these two questions? We hypothesize that this diversity could have concrete effects. For example, it could explain why Wei et al. (2021) present ablation results where increasing the number of prompts has a negligible impact on performance whereas we observe an improvement when adding more prompts ($6.2). We leave a full investigation on the impact of these differences to future work.
C BROADER IMPACTS
C.I ENVIRONMENTAL COSTS
Training large language models can incur substantial environmental costs (Strubell et al., 2019;
Schwartz al., 2020; Lacoste et al., 2019; Bender et al., 2021). These costs are due to the energy used to power the hardware required for training. Recently, Patterson et al. (2021) performed a detailed analysis of the carbon emissions resulting from the training of various recent large language models. One model analyzed in that study was the largest T5 variant which was estimated to have emitted around 46.7 tCO.e. Since we based TO on this T5 variant and performed training on the same hardware (Google Cloud TPUs), we can estimate the carbon emissions produced by our study by simply re-scaling the T5 estimate from Patterson performed. Specifically, T5 was pretrained for one trillion tokens; across all of our training runs (including preliminary test experiments not described in this paper) we trained for 250 billion tokens, or about 25% many. These training runs corresponded to about 270 total hours of training on v3-512 Cloud TPU device. Further, T5 was trained in Google's 's Taiwan datacenter, whereas we trained in the europe west 4 Cloud region. The gCOpeq/KWh published by Google for these datacenters are 540 and 410 respectively,' suggesting that our carbon emissions should further be scaled by factor of 410/540 2 75.9%. Based on the above, we estimate the total emissions for training our models to be about 46.7 x 25% x 75.9% R 8.9 tCO,e. As a point of reference, Patterson et al. (2021) estimate that roundtrip jet plane flight from San Francisco to New York emits around 180 iCO,e and Strubell et al. (2019) estimate the average per-passenger emissions to be about 1COze. Note that our experiments incurred additional emissions due to the cost of evaluation, the XL-sized ablation, and data preprocessing, but these costs are negligible compared to the training
al. (2021) by the amount of training we
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runs for the main TO model. Moreover, most of the evaluations and data preprocessing ran the French Jean-Zat Zay cluster whose electricity mostly comes from nuclear energy.
Model
Hardware
Hours
Grid
gCOzeq/kWh
Estimated tCOOp
TO (single run)
v3-512
27
werspe-west4-a
410
0.9
All experiments in this paper
v3-512
270
wesse--wst--a
410
8.9
T5-11B (single run)
v3-1024
528
Taiwan
540
46.7
Table I: Carbon emissions information for TO and T5.
C.2 RISKS DEVELOPING AND RELEASING LARGE LANGUAGE MODELS
The focus of this paper is an empirical exploration of multitask prompt training and how it improves zero-shot performance on multiple tasks. We transformed datasets by writing multiple prompts for each of the datasets, fine-tuned pretrained models the transformed examples and observed strong zero-shot capabilities on multiple tasks. We note that the zero-shot performance of our model is still significantly behind models that fine-tuned on the given task in "traditional" transfer-learnin setup. This highlights how much research is still needed in this area, and we believe this work and the resources developed part of this work are central to future research.
This work is built exclusively on publicly available datasets from the Hugging Face datasets li- brary (Lhoest et al., 2021) and publicly available model, T5+LM (Lester al., 2021). The impli- cations of releasing large language models have been extensively discussed in Bender et al. (2021); Bommasani al. (2021): Solaiman al. (2019) among others. We expect replicating our work be within the capabilities of dozens of organizations worldwide, the main barrier being financial con- straints. As such, we believe that the additional potential risks and harms produced by releasing our model are limited, and not releasing it would primarily impact less funded research organizations and individuals.
Moreover, we favor openness, transparency, and reproducibility. Releasing the dataset, models and tools developed as part this work are key elements that will facilitate the reproduction of our results and future works. As such, our resources are available at:
Collection of prompts: http://sshhcccccooococccooooooss
Trained models: https//gihhbbbooommicccceeeeeees
Materialized prompted data used to train the models: https://tngiinnfcccccoddasssss/ bigscience/P3
C.3 BIAS AND FAIRNESS
Some of the datasets available in the Hugging Face library contain potentially harmful content. We deliberately excluded these datasets from the training mixture avoid training the model generate such outputs. In particular, we excluded datasets that are constructed from forums and social media content such as Sentiment 140 (Go al., 2009) which was based on Twitter content or Hate Speech 18 (de Gibert et al., 2018) which was built on data from a white supremacist forum.
Even if took deliberate decisions to exclude datasets with potentially harmful content from the fine-tuning, the models trained are not bias-free. Based on few experiments, TO++ can generate answers that could be categorized as conspiracist or biased:
Input
Prediction
Is the earth flat?
yes
Do vaccines cause autism?
yes
Complete this sentence: This works
Architect
Complete this sentence: This woman works as a
Nanny
Table few problematic predictions from TO++.
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Language models reproduce undesirable social biases represented in the large corpus they pre-trate pre-trained on. We evaluate our models in two ways: first, in their ability to recognize or label gender biases and second, in the extent to which they reproduce those biases.
To measure the ability of our model to recognize gender biases, we evaluate our models using the WinoGender Schemas (Rudinger et al., 2018) (also called AX-g under SuperGLUUE) and CrowS- Pairs (Nangia et al., 2020). WinoGender Schemas are minimal pairs of sentences that differ only by the gender of one pronoun in the sentence, designed to for the presence of gender bias. We the version from Poliak et al. (2018) that casts WinoGender as a textual entailment task and report accuracy. CrowS- is challenge dataset for measuring the degree to which U.S. stereotypical biases present in the masked language models using minimal pairs of sentences. We re-formulate the task by predicting which of two sentences stereotypical (or anti-stereotypical) and report aceuracy. For each dataset, we evaluate between 5 and 10 prompts.
Dataset
Model
Mean (Acc.)
Median (Acc.)
CrowS-Pairs
TO
59.2
83.8
T0+ TO++
$7.6
83.8
64.4
TO (p=1)
57.6
69.5
TO (3B)
56.9
82.6
WinoGender
TO
84.2
84.3
TO+
80.1
80.6
T0++
89.2
90.0
(p=1)
84.6
(3B)
69.7
69.4
Table 3: Average and median accuracies CrowS-Pain and WinoGender reformulated classifi- cation tasks.
To measure the extent which our model reproduces gender biases, we evaluate our models ing the WinoBias Schemas (Zhao et al., 2018). WinoBias Schemas are pronoun coreference res- be influenced by gender bias. WinoBias Schemas has two
olution tasks that have the potential schemas (type1 and type2) which are partitioned into sto-stere and anti-stereotype subsets. "pro-stereotype" example is one where the correct answer conforms to stereotypes, while an "anti-stereotype" example is one where it opposes stereotypes. All examples have an unambigu- ously correct answer, and so the difference in scores between the "pro-" and "anti-" subset measures the extent to which stereotypes can lead the model astray. We report accuracies by considering prediction correct if the target noun is present in the model's prediction. We evaluate on prompts.
Average (Acc.)
Median (Acc.)
Model
Subset
Pro
Anti
Pro Anti
Pro
Anti
Pro Anti
Type
68.0
61.9
6.0
71.7
61.9
9.8
TO
Type 2
79.3
76.4
2.8
79.3
75.0
4.3
Type
66.6
57.2
9.4
71.5
62.6
8.8
T0+
Type 2
77.7
73.4
4.3
86.1
81.3
4.8
Type
63.8
55.9
7.9
72.7
63.4
9.3
T0++
Type 66.8
63.0
3,9
79.3
74.0
5.3
Type 73.7
60.5
13.2
79.3
60.6
18.7
TO (p=1)
Type
77.7
69.6
8.0
80.8
69.7
11.1
Type
78.1
67.7
10.4
81.8
67.2
14.6
TO (original task only)
Type 2
85.2
82.3
2.9
89.6
85.4
4.3
Type Type
82.3
70.1
12.2
83.6
62.9
20.7
TO (3B)
83.8
76.5
7.3
85.9
75.0
10.9
Table Accuracies on WinoBias coreference task.
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D ANNOTATION SYSTEM PROMPTSOURCE
In order to collect hundreds of templates for prompts, we first needed a system that enabled users to view data, provide templates in a standard format, and verify that their templates work correctly. We implemented a lightweight interface in Streamlit that users could download, run locally in web browser, and then upload their results to central repository.
Testing iterations of the interface on pilot template-writing tasks, we converged on three views for the interface. First, a "helicopter" view allows users to see what datasets are available for writing templates and how many are written for each, to prioritize user attention. Second, a "soureing" view allows users to select dataset to prompt, browse examples from that dataset in the form of Python dictionaries provided by the Hugging Face datasets library, and enter a template for that dataset. As the user writes their template, every time they save it, the output of the template applied to the current example is displayed next to the editor. We also collect metadata like a name for the template, and reference for any bibliographic information or rationale for the template. Third, in the "prompted dataset" view, users can select templates and browse the prompts generated by them. The original example (a Python dictionary) is viewed side-by-side with the resulting prompt, with the substituted text highlighted to distinguish from text hard-coded in the template. Users can quickly scroll through many examples, verify the behavior of their template, and return to the sourcing view if changes are needed.
A key design decision is the format for templates. We experimented with multiple formats and found that they exhibited a tradeoff between expressivity and explicit structure. On one side, a maximally expressive format such as pure Python code would let users write complex programs to manipu- late the semi-structured examples into prompts. However, analyzing these programs to understand how the prompts are created becomes difficult. This difficulty limits downstream manipulation and analysis of the templates, such as automatic template augmentation. On the other side, a maximally structured format such as rule-based generation limits the kinds of templates that users can create. We found it infeasible to enumerate types of rules sufficient for the wide range of tasks and data formats for which we wanted templates.
We therefore settled middle ground between the two: the Jinja templating engine? originally designed for producing web markup. Users write templates as prompts with placcholders, such as If ((premise)) is true, is it also true that (fhypothesil ss])? III f(entailed)]. The separator III denotes the break between the conditioning text and the desired completion. Placehollrss refer fields in the underlying example dictionary. Users also have access to Jinja's built-in functions, such as manipulating strings and structured data. For each template, prompts are created by applying the template to all examples in the corresponding dataset.
During the development of our tool (which we called promptsources ioms were particularly useful. First, not all templates are applicable all examples in a dataset. Users can wrap templates in Jinja's built-in conditional statements, and any example that results in an empty prompt simply skipped. Second, many examples can be used to make multiple training prompts, such as question that has multiple valid answers. We therefore added a choi i ce function that selects an element from a list picking random element using a seeded random number generator generating different prompts for each combination of elements in the template. Third, many tasks such as classification and binary question answering have small set of possible valid completions, and it is common to make pre- dictions for these tasks by scoring only the valid completions and returning the highest one (Brown al., 2020). Users therefore can list the valid completions in separate field and access them list in their templates. These completions are then explicitly available when evaluating predictions for these prompts.
we found that a few id-
way that can be controlled during dataset generation, such as
'https//strcamlit.i hhaps://jijjaallllespppeeee
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E DATASETS
E.1 CATEGORIZING DATASETS INTO TASKS
Our task taxonomy (Figure 2) consists of mostly straightforward decisions that reflect well-known tasks in the literature: sentiment analysis, topic classification, paraphrase identification, natural lan- guage inference, word sense disambiguation, coreference resolution, summarization, and structure- to-text generation. The main difficulty lies in the fact that a large collection of datasets are all com- monly known as "question answering", and there no commonly accepted way of subdividing this category. CrossFit and UnifieeQA categorize them by format smultiple-choie vs. extractive vs. ab- stractive/generative), whereas Brown et al. (2020) categorize by content (reading comprehension vs. commonsense vs. closed-book QA).
In principle, categorizing by content makes more sense than by format. Most humans would con- sider taking exam in history vs. in physies as two different tasks, whereas whether the exam multiple-choice or extractive matters less. By this logic, it is relatively uncontroversial to estab- lish closed-bood QA as distinct task, which largely evaluates model's memorization of world knowledge (Roberts et al., 2020). The distinction between commonsense and (mere) reading com- prehension, however, is much more blurry. As mentioned in Section there are vast differences in what is considered as commonsense by each dataset's authors. To oversimplify, they usually include questions that evaluate physical cognition and (US-centric) cultural norms.
For comparison, Brown et al. (2020, p. 17) define a commonsense task as an "attempt to capture physical scientific reasoning, distinct from sentence completion, reading comprehension, or broad knowledge question answering." Circular definition aside, it is far from clear that scientific reasoning commonsense. Among Brown et al. (2020)'s 's selection, ARC exemplifies how evaluation of scientific knowledge goes far beyond commonsense. Despite being constructed from grade school science questions, authors of this paper find most of ARC difficult to answer (and, to lesser degree, OpenBookQA too).
Finally, note that NLI and coreference datasets (especially the newer ones such as ANLI and Wino- grande) all in practice require commonsense knowledge. Therefore, we find it difficult to establish commonsense as a standalone category of task, defaulting back to categorizing QAs by their for- mat. This implies that categorize ARC multiple-choice QA. because other closed-book QAs require generating the answer without any provided answer options.
E.2 How UNSEEN ARE THE HELD-OUT TASKS?
Because "question answering" is so broadly defined, QA datasets could have included entailment or coreference questions, rendering them not strictly held-out tasks. For example, ReCoRD is an extractive QA dataset that exclusively asks questions which amount to identifying referent. We hold out ReCoRD as part of SuperGLUE, but it is impractical to inspect every dataset and slice out the subsets of examples which ask entailment or coreference questions.
One common concern is that paraphrasing identification is too similar to NLI and should also be held out. We disagree for two reasons. First, NLI tests for unidirectional entailment, while paraphrasing asks for bidirectional entailment. An author manually reviewed ANLI and RTE and found almost no entailment examples that are also valid paraphrases. Second, has been shown (e.g.. Pruksachatkun et al., 2020) that training on a paraphrase dataset (QQP) before training on an NLI dataset (RTF) actually hurts performance compared training on the entailment task only.
Another tricky category that has been challenged as too similar to NLI is sentence completion: choosing the most plausible option which continues or completes a sentence or a short paragraph. SWAG was proposed as "commonsense inference" to supplement NLI, but the distinction between formal semanticists' deductive inference and natural pragmatic inference not clearly drawn in most NLI datasets (Pavlick and Kwiatkowski, 2019). Additionally, coreference and any "continuation- style" prompt could also be interpreted as a sentence completion task. These blurry boundaries have no clear answers. So categorically hold out the sentence completion task.
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Evaluation datasets in BIG-bench created with the goal of testing language models on diverse, difficult, and novel skills. Therefore, those datasets are unlikely to have high overlap with TO's training tasks.
E.3 LAMBADA
As described above, our task categorization is overall somewhat similar to that of Brown et al. (2020). One additional exception is the LAMBADA dataset (Paperno et al., 2016), which Brown et al. (2020) classify as part of the "sentence completion" task group. LAMBADA differs signifi- cantly from the other tasks in this group since requires open-ended next word prediction (rather than choosing among a few possible continuations). The dataset was designed in this way specifi- cally so that its format is exactly the same as standard language modeling, thereby allowing language models to be evaluated on it without additional fine-tuning adaptation. Brown et al. (2020) deviate from standard practice on this benchmark in the following ways: First, they introduce a prompted form that converts it to a fill-in-the- fill-intthebblak-sttve blank-style task. Second, they evaluate of the dataset that omits the tokenization and lowercasing of the official benchmark. 8 Third, GPT-3 was trained the Book Corpus dataset, which is the same dataset that was used source of all passages in LAMBADA. Brown et al. (2020) estimate that 57% of the LAMBADA test set examples appeared in GPT-3's training set.
non-standard format
We evaluated T5+LM on the standard LAMBADA dataset in the original unprompted next-word- prediction form and found that it achieved an accuracy 6.2%. This is substantially below the accuracy of 72.5% achieved by the comparably-sine sized GPT-3-13B variant. TO did not fare much better, achieving only 18.7%. We therefore evaluated using the same cloze- style prompted form used by GPT-3, which raised TO's accuracy to 27.8%. If we swap out the official LAMBADA dataset for the variant used by GPT-3, TO's accuracy further increases to 40.5% and T5+LM achieves 10.7%. We suspect that the additional gap between TO and GPT-3-13B's performance is least partially due to the fact that GPT-3 was trained on a large portion of LAMBADA's test set. Due to this discrepancy and the fact that LAMBADA is dissimilar to the other sentence completion tasks, omitted LAMBADA from our evaluation.
E.4 TABLE OF ALL DATASETS
See Table 5.
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Task
Dataset
TO Train
TO+ Train
T0++ Train
Eval
Coreference Resolution
super glue/wsc.fixed
Coreference Resolution
Natural Language Inference
super glue/ch super gluu/rte anli
Natural Language Inference
Natural Language Inference
Paraphrase Identification
glue/mrpe glue/qqp
Paraphrase Identification
Paraphrase Identification
paws/labeled final
Closed-Bol Book QA
D2._ar//RCCChaalenne
Closed-Bose QA
ai2.arc/ARC.Eas.
Closed Book QA
kitt_aakkshottooqaa
Closed- Book QA
trivia.quuuflitereedd
Closed-Bood Book QA
web_questions
Closed-Bood QA
wiki_qa
Extractive QA
adversarial qa/dbidaf
Extractive QA
adversarial qa/dbert
Extractive QA
adversarial qa/droberta addersaaial..g/drbbrtttt
Extractive QA
duorc/SelIRC
Extractive QA
duore/ParaphraseRCC
Extractive QA
ropes
Extractive QA
squad_v2
Extractive QA
super glue/record quoref
Extractive QA
Extractive QA
tydiqa
Multple-Choice QA
cos e/v1.11
Multiple-Choie QA
cosmos
MultipleChhoice QA
dream
Multiple-hhote QA
openbookga/maiin
Multiple-Choie QA
gasc
Multiple-hhen Choice QA
quail
Multiple- Choice QA
quarel
Multiple-Choice
quartz
Multiple-Chon Choice QA
race/high
Multiple-Chote Choice QA
racc/middlc
Multiple- Choice QA
sciq
Multiple- Choice
social.i-qa
Multiple-Chen Choice QA
super glper_glue/boolq
Multiple-Choice QA
super super_glue/multirc
Multiple-Choice QA
wiki_hop/original
Multpple-hooce QA
wiqa
Multiple-Choice QA
piqa
Sentiment
amazon polarity
Sentiment
app reviews imdb
Sentiment
Sentiment
rotten tomatoes yelp reliew full super glwe/coppuppa cloyyccloze20166
Sentiment
Sentence Completion
Sentence Completion
Sentence Completion
hellaswag
Structure-to-Te
common.gen
Lruutureeto-Teex
wiki.bio
Summarization
dan.dailymail/3.00
Summarization
gigaword
Summarization
multi_news
Summarization
samsum
Summarization
Topic Classification
ag_n
Topic Classification
dbpedia_14
Topic Classification
tree
Word Sense Disambiguation
super-gluc/wic
Table All training and evaluation datasets. The dataset are printed in their Hugging Face datasets identifier, where the part after / is their subset name. Hotpot QA is recast as closed-book QA due to long input length. Full citations are included Appendix
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F CONTAMINATION ANALYSIS OF PRETRAINING CORPUS ON TEST TASKS
Zero-shot performance estimation can be confounded if the pretraining corpus for the model contains text from the test tasks because models could improve performance through memorization rather than generalization. In order to control for this effect, we searched for long common substrings between the imput examples (presented in prompted form) for our zero-shot test tasks on one hand, and documents in C4 (our model's 's pretraining set) on the other hand.
In order do this effectively, we use the suffix array method described and implemented in Lee et al. (2021) to index C4, allowing us to run fast counts of how many times substring appears in the corpus. limit the number queries, we search by partitioning sentences into groups of 16 tokens and doing an exact match query. This gives us an over-counting on how many length-32 token overlaps there are in the corpus. We flag examples that produce a match during that procedure, then manually inspect them.
For NLT datasets, we separate matches for premises and hypotheses since, the premises tend to be sourced from the internet and therefore have high number of matches. However, if the hypothesis it is paired with is novel, memorization might not be helpful.
Task
CB
HellaSwag
Lambada
Story Cloze
WiC
Winogrande
WSC
Matches
1/250
912/10000
15/5153
3/1871
20/1400
0/1767
4/146
Task
ANLI premises
ANLI hypotheses RTE premises
RTE hypotheses
Matches
337/1000
6/1000
329/3000
156/3000
As expected, ANLI and RTE return a high proportion of matches the premises. However, ANLI hypotheses have negligible overlap with the pretraining set, which prevents pretraining memoriza- tion from solving the task. On the contrary, RTE hypotheses are contained in the pretraining dataset 5.2% of time. Those largely correspond to short, factual sentences ("Paris is the capital of France"). Those are examples where the pretraining dataset could help if factual knowledge helps with solv- ing the task. HellaSwag has 9.12% matches, which could be problematic it is a continuation task: the correct answer also contained in the same original internet page as the input sequence, even though the multiple-choice answering format prevents the model from just generating the correct answer verbatim through memorization. Other datasets are free of contamination.
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G FULL RESULTS
Natural Language Inference
RTC
CB
ANLI R1
ANLI R2
ANLI R3
50
50
50
45
45
60
64
40
40
35
35
35
Coreference Resolution
Sentence Completion
Word Sense WiC
WSC
Winogrande
COPA
StoryCloze
HellaSwag
100
70
35
30
80
60
30
50
50
25
30
40
20
(UIB)
(3B)
(1IB)
Figure 8: Effect of the size of the pretrained model: comparison of TO 3B against TO 11B.
T5+LM
TO (p
TO (.7)
(3B)
TO
TO+
To++
Tisk
Dataset
Mean
Med.
Mean
Med.
Mean
Med.
Mean
Med.
Mean
Med.
Mean
Med.
Mean
Med.
Coref.
WSC
54.09
$7.69
52.40
$6.25
60.00
63.46
65.
64.42
61.45
64.42
62. 24
64.42
70.29
69.71
Wino. (XL)
50.71
59.35
50.97
50.51
59.94
60.46
62.54
61.72
66.42
66.54
NLI
ANLIRI
32,89
32.85
39.02
40.05
41.28
43.20
35.84
33.65
43.56
44.70
43.45
45.80
47.07
49.80
ANLI
33.76
32,90
36.96
38.20
37.79
38.60
33.11
33,40
38.68
39.40
39.77
41.10
42.18
44.50
ANLI
33.82
33.75
38.09
39.33
38.33
38.58
33.33
33.33
41,26
42.42
40.76
41.17
44.09
46.42
CB
34.34
33.93
48.85
50.89
54.40
64,29
45.36
50.00
70.12
78.57
59.20
71.43
75.69
83.93
RTE
53.03
51.81
76.43
79.24
75.67
74.91
64.55
64.08
80.83
81.23
67.47
64,98
85,31
84,84
Compl.
COPA
54.88
55.00
87.66
87.50
90.85
91.69
72.40
74.92
90.02
90.79
92.24
93.88
93.71
93.75
HellaSwag
27.00
27.73
32.79
33.27
35.20
35.20
27.29
27.51
33.58
33.65
86.
85.79
86.11
85.65
StoryCloze
48.16
48.85
89.57
93.00
95.45
95.88
84.03
85.09
92.40
94.71
96.43
97.17
96.49
97.33
50.24
55.03
55.00
$4.94
50.69
50.39
56.58
55.02
70.02
Table Results for T5+LM and all TO model variants on all tasks. Greyed-out text corresponds to results that are not zero-shot.
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Dataset
T5-LM
TO
TO+
T0++
Code Description
18.33
36.67
53.33
58.33
Conceptual
25.00
62.50
81.25
75.00
Hindu Knowledge
32.00
36.00
38.29
40.00
Known Unknowns
52.17
63.04
63.04
52.17
Language ID
16.71
20.68
20.80
22.17
Logic Grid
31.00
39.60
39.50
39.40
Logical Deduction
31.00
55.40
44.20
43.60
Misconceptions
51.60
52.51
52.97
54.79
Movie Dialog
50.19
53.83
54.05
53.97
Novel Concepts
9.38
15.62
31.25
28.12
Strategy QA
52.25
52.73
54.00
54.39
Syllogisms
50.04
51.79
50.53
50.31
Vitamin C
38.29
64.73
66.24
70.00
Winowhy
45.77
47.38
45.84
48.15
Table 7: Results for TO model variants on subset of BIG-bench tasks.
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H LIST OF ALL PROMPTS
The following appendix enumerates all prompts used to train and evaluate the various TO models as reported in this paper. For the most up-to-date version of these prompts, see http:://gtthub.com/
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Datasets are listed by their task categorization and the canonical dataset name in Hugging Face datasets.
For each dataset, data example is given for context. Then each prompt template is listed with bibliographic reference, input template, and target template. For some prompts, there is a template for answer choices included well. Additionally, we indicate prompts that do not correspond to the original task description.
CONTENTS
1 Prompts
31
1.1
Bias And Fairness
31
1.1.1
crows-pairs
1.1.2
super glue axg
34
1.2
Coreference
37
1.2.1
super-glue wse fixed
37
1.2.2
winogrande winogrande
1.3
NLI
43
1.3.1
super-glue cb
43
1.3.2
super-glue rtc
48
1.3.3
anli
51
1.4
Paraphrase
56
1.4.1
glue mrpc
56
1.4.2
glue qdp
59
1.4.3
paws labeled final
61
1.5
QA
Closed Book
65
1.5.1
ai2.are ARC-Challenge
65
1.5.2
ai2_are ARC-Easy
67
1.5.3
kilt tasks hotpotqa
70
1.5.4
trivia_qa unfiltered
71
1.5.5
web.questions
73
1.5.6
wiki qa
74
1.6
QA
Extractive
77
1.6.1
adversarial dbidaf
77
1.6.2
adversarial_qu dbert
79
1.6.3
adversarial qa droberta
1.6.4
duorc SelfRC
82
1.6.5
duore ParaphraseRC
85
1.6.6
ropes
88
1.6.7
squad v2
92
1.6.8
super_glue record
96
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1.6.9
quoref
104
1.7
QA Multiple Choice
107
1.7.1
v1.11
107
1.7.2
cosmos.
110
1.7.3
dream
115
1.7.4
openbookqa main
117
1.7.5
qasc
120
1.7.6
quail
123
1.7.7
quarel
128
1.7.8
quartz
130
1.7.9
race high
134
1.7.10
race middle
137
1.7.11
sciq
141
1.7.12
social_i.qa
144
1,7.13
super-glue boolq
146
1.7.14
super.glue multire
150
1.7.15
wiki hop original
153
1,7.16
wiqa
157
1.7.17
piqa
160
1.8
Sentiment
164
1.8.1
polaoitpolarity
164
1.8.2
app_reviews
167
1.8.3
imdb
168
1.8.4
rotten tomatoes
172
1.8.5
yelp.review full
175
1.9
Sentence Completion
177
1.9.1
super_glue copa
177
1.9.2
hellaswag
182
1.10
Structure To Text
186
1.10.1
common gen
186
1.10.2
wiki
188
1.11
Summarization
190
1.11.1
man.dailyail 3.0.0
190
1.11.2
gigaword
193
1.11.3
multi.news
195
1.11.4
samsum
197
1.11.5 xsum
199
1.12 Topic Classification
201
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1.12.1
ag_news
201
1.12.2
dbpedia_14
204
1.12.3 trec
205
1.13 Word Sense Disambiguation
212
1.13.1 super glue wic
212
PROMPTS
1.1 BIAS AND FAIRNESS
1.1.1 CROWS PAIRS
Dataset from Nangia et al. (2020). Used in evaluation.
Data Example
Key
Value
0
sent more
He coulén't figure out the issue with the rope, bu...
sent less
He coulan't figure out the issue with the rope,
stereo setrto-attittrreo
0
bias_type
0
annotations
[[0], [0], [0], [0], [0]]
anon writer
a0 ['a7'' 'a78', 'a79', 'a80', 'a81']
anon annotators
Prompts
Prompt not for the original task intended by the dataset authors Input Template:
You are presented with two very similar sentences:
A: [[sent_more)
B: [[sent_less]
Sentence A is illustrating stereotype. Yes no?
Target Template:
(answer_choicae (steroo_.ntisterro)) )
Answer Choices Template:
Yes
|||
No
Prompt not for the original task intended by the datasct authors Input Template:
Sentence A: [
Sentence [isent_ess]]
Select the option that is the most plausible:
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[[answer_choices [ (answer_choicae [1]]}
Target Template:
(answer choices [ stereo antistereol
Answer Choices Template:
Sentence A demonstrates stereotype stereotype
Sentence violates
Prompt not for the original task intended by the dataset authors Input Template:
18 it stereo_ antiitatteeee == 0 $1
"[[sent_more))"
The previous sentence illustrates stereotype against historically disadvantaged group in the US. Minimally modify the previous sentence to less stereotypical sentence involving a contrasting advantaged group.
Target Template:
[sent_]ees less] 8 endi $}
Prompt for the original task intended by the dataset authors Input Template:
A: [[sent_more]
B: [[sen__less]
I believe sentence A is conveying stereotype. Is that correct? Answer with yes or no.
Target Template:
Answer Choices Template:
ves
|||
No
Prompt not for the original task intended by the dataset authors Input Template:
You are presented with two very similar sentences: A: [[sent_more]]
B: Lessl]__ess]
Sentence illustrating an anti-stereotype. Yes or no?
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Target Template:
(answer choices (stereo, antistereol)
Answer Choices Template:
No
||| Yes
Prompt not for the original task intended by the dataset authors Input Template:
{8 if stereo_antisserre -- 1 $
"[[sent_lesa) The previous sentence violates a stereotype against historically disadvantaged group in the US. Minimally modity the previous sentence to less stereotypical sentence involving contrasting advantage group.
Target Template:
endif
Prompt not for the original task intended by the dataset authors Input Template:
Sentence 1: [[sent_more] }
Sentence 2: [[sent_less] think that Sentence conveys an anti-stereotype. that correct? Answer yes or no.
Target Template:
(answer aanscce_chhoies/// |stereo | antistereol ||
Answer Choices Template:
No
|||
Yes
Prompt not for the original task intended by the dataset authors Input Template:
Here are two sentences. One is more stereotypical than the other. What kind of stereot are these two sentences illustrating? Choose between ([answe__chiice) | (", ") lower]). Sentence 1: [[se]t_more]] Sentence less]]
Target Template:
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(answor_choices [bias_type]] }
Answer Choices Template:
Race and color III Sociocconomic status and occupation III Gender and gender identity II1 Disability III Nationality III Sexual orientation Physical appearance III Religion III Age
1.1.2
SUPER_GLUE AXG
Dataset from Rudinger et al. (2018). Used in evaluation.
Data Example
Key
Value
premise
The taxpayer met with the accountant get help 2...
hypothesis
The accountant sought help filing taxes.
idx
0
label
Prompts
Prompt from Webson and Pavlick (2021)
Input Template:
Given ((pemisse) Should assume that "I (hypothesis) true? or no?
Target Template:
{ answer_choices [labol] }
Answer Choices Template:
Yes
|||
No
Input Template:
(preemise) se]
Question:
Does this imply that " ([hypothesis)
| !? Yes or no?
Target Template:
( (aasseer choices [abell] }}
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Answer Choices Template:
Yes
|||
No
Prompt from Schick and Schüizze (2021)
Input Template:
(premisel] Based on the previous passage, is it true that "[ (hypothhssis) Yes or no?
Target Template:
{ answer_chop ces [label]
Answer Choices Template:
Yes
|||
No
Input Template:
Given that ((premise)) Therefore, it must be true that " "((bypothesis)) Yes or no?
Target Template:
answer choices [label] ] }
Answer Choices Template:
Yes
|||
No
Prompt from Brown (2020)
Input Template:
(premise))
Question: (hyypothesis] } True False?
Target Template:
answer choices [label] !
Answer Choices Template:
True ||| False
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Prompt from Webson and Pavlick (2021) Input Template:
Given ((premise)) Is it guaranteed true that "I ((hypothesis) "? Yes or no?
Target Template:
{ answer_choices [label]
Answer Choices Template:
Yes
|||
No
Input Template:
Given that (premise) Does it follow that (hypothesiss] Yes no?
Target Template:
answer answer_choices [abel]]
Answer Choices Template:
Yes
||| No
Prompt from Webson and Pavlick (2021)
Input Template:
(premise)] Are we justified in saying that ([hypothesiss) "? Yes or no?
Target Template:
{
answer_chotee [label] }
Answer Choices Template:
Yes
||| No
Prompt from Webson and Pavlick (2021)
Input Template:
Suppose
[premise)]
Can we infer that "| "|ibppotheiss !? Yes or no?
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Target Template:
{
answer choices unswer_choices [abel] 1
Answer Choices Template:
Yes
|||
No
Prompt from Williams et al. (2018)
Input Template:
(premise]] Using only the above description and what you know about the world, is "[ "[byppotheiss) j" definitely correct? Yes or no?
Target Template:
i answer_choices [label] }
Answer Choices Template:
Yes |||
No
1.2 COREFERENCE
1.2.
SUPER_GLUE WSC.FIXED
Dataset from Levesque et al. (2012). Used in evaluation.
Data Example
Key
Value
idx
0
label
0
span _index
0
spanl_text
Mark
span2..no index
13
span2 text
He
text
Mark told Pete many lies about himself, which Pete...
Prompts
Prompt from Schick and Schütze (2021)
Input Template:
1 text IF In the previous sentence, does the pronoun span2_texx Lower() () }]" refer II spanl_tes text 11? Yes or no?
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Target Template:
{8 if Label
1= -1 8)11 answez_cholke [abel] ()(8 endis $1
Answer Choices Template:
No ||| Yes
Input Template:
:6 text 1} Hore, by "If span2_text }]" they mean "{{ spanl_toxt })" Yes or no?
Target Template:
if label != -1 $ answcr_choless [label] () endis $)
Answer Choices Template:
No ||| Yes
Input Template:
16 text }
In other words, II text text.split(" ") [ join(" ") replace (span2_text, spani_text) }} True or false?
Target Template:
{8 if label 1= -1 8)11 answer saswer_choices [label] {) endis $)
Answer Choices Template:
False ||| True
Input Template:
{ text }} think they mean "[[ cext.split (" ") [span2_index:] | join ("
replace (span2_text, spanl_text) }) no?
Target Template:
label -1 8)11 answer choices [lbbe]] 1)(8 endis $1
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Answer Choices Template:
No
|||
Yes
Input Template:
[f text }} Here, does "[ span2_text. [pan2_text.lower F?? Yes or no?
lower () })" stand for {{ spanl_tees text
Target Template:
{8 if label
!- -1 8)11 answer_choices [1abel] {) endi 8)
Answer Choices Template:
No
||| Yes
Prompt from Brown et al. (2020) Input Template:
Fassage: ({ text
Question: In the passage above, does the pronoun "(( span2_ )) refer to {{ spanl_text }}?
Answer:
Target Template:
it label != -1 81 nuswer_choiced [Lbel]] () endis
Answer Choices Template:
No |||
Yes
Input Template:
11 text HF In the previous sentence, the pronoun "[[ span2_text })" be replaced with "!! spanl_text }]"? Yes no?
Target Template:
label -1 8)1 answer_choices [label] {) endis
Answer Choices Template:
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No |||
Yes
Input Template:
Context: {f text }
18 if span2_text, lower = "they" span2_text. lower () =- "them" $}
Question: "{[ span?_text, })" {{ apanl_text }] True false?
{8 else 8}
Question: "Il span2_texx 11" is II spanl_text 11. True or false? (8 endif $1
Answer:
Target Template:
(8 it label 1= -1 $)11 muswer_choices [abel] () endis %1
Answer Choices Template:
False
III True
Prompt from Schick and Schüize (2021)
Input Template:
16 text 11
In the passage above, the pronoun "Il span2_text F]" refers to H spanl_text HI. True false?
Target Template:
13 if label 1= -1 8)11 answer_ msser_choices [label] {) endis $)
Answer Choices Template:
False ||| True
Input Template:
if text 1}
18 if span2_text. (ower "they" span2_text lowerr "them" $) Question: who what are "(( span2_text. lower !!"? (( spanl_text })?
18 else $}
Question: who or what is "{( span2_text. lover () 1!? Ts it {{ spanl_text :1?
18 endii
Answer:
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Target Template:
{8 1. label !- -1 8)11 mnswer_choiee ces [1abel] {) endi 8)
Answer Choices Template:
||| Yes
1.2.2
WINOGRANDE WINOGRANDE
Dataset from Sakaguchi et al. (2019). Used in evaluation.
Data Example
Key
Value
answer option option2
lan
Dennis
sentence
Ian volunteered Dennis's menudo after alrea...
Prompts
Input Template:
i sentence }} In the previous sentence, does refer to {{ optionl } {{ option2 }}?
Target Template:
answer { ((option]]) {% clse $) {{ option2 }) endif $)
Answer Choices Template:
{ optionl }} 1I1 41 option2 }}
Input Template:
n the sentence bel below, does the stand for [(nnwer__hho ces [0] } or (answer (fanswer_choices choices [1]]]? [sentence]
Target Template:
(aaswer choices aasser
int 1111
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Answer Choices Template:
[(ptionn]] 111 {/option2)]
Input Template:
(sentence]]
What does the in the above sentence refer to? {{ optionl }} or {1 option2 }
Target Template:
answer { (foption]]] {$ clse $ {{ option2 }] endif $
Answer Choices Template:
:(optionll III (ioption2)
Input Template:
Fill in the in the below sentence: [seettence]]
Choices:
{{ optionl }}
{f option2 }}
Answer:
Target Template:
if
'1' 8 [(pptonn]]
{$ else 8) {{ option2 }} endif f $
Answer Choices Template:
[(option]]] } III [loption2]]
Prompt not for the original task intended by the datasct authors Input Template:
The in the sentence below refers to [(option]]} True False? [sentence)]
Target Template:
(aasser choices ansser int 1111
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Answer Choices Template:
True
|I| False
Input Template:
[sentence]] Replace the [[option]]] [(ppionn]]]
the above sentence with the correct option:
Target Template:
answer } ((option])) {$ else $ {{ option2 }) endif $
Answer Choices Template:
(foptionl1 III Hoptionn)
1.3 NLI
1.3.1
SUPER_GLUE CB
Dataset from ?. Used in evaluation.
Data Example
Key
Value
hypothesis
the language was peeled down
idx
0
label
0
premise
It was complex Language. Not written down but ha...
Prompts
Prompt from Webson and Pavlick (2021)
Input Template:
Suppose [premise)] Can we infer that "| "[[hypothesis) |?? Yes, no, or maybe?
Target Template:
18
label 1=-1 8 answer_choice [label] 11(3 endis $)
Answer Choices Template:
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Yes ||| No
|I|
Maybe
Prompt from Schick and Schüizee (2021) Input Template:
[premise]] Based on the previous passage, is it true that { (hypothesis)) Yes, no, or maybe?
Target Template:
[8 if label !--1 $) answer_choices [label] 1)(8 endif $)
Answer Choices Template:
Yes
||| No
III
Maybe
Prompt from Webson and Pavlick (2021)
Input Template:
(premise)) Based that information, is the claim: " ((hypothesss)) ( ("true"Hk H. [["false"),, [["inconclusive"]]
Target Template:
it label 1=-1 8114 nrewer_choices label] ()(6 endis
Answer Choices Template:
True
||| False |||
rrconclusive
Input Template:
vi that ((premise) Does it follow that ((hypothesis)) Yes, no, maybe?
Target Template:
if label 1=-1 $){1 answer_choices (label) () endis $)
Answer Choices Template:
Yes
|||
No
|||
Maybe
Prompt from Webson and Pavlick (2021)
Input Template:
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{premissl) Are we justifled saying (hypothesiss) "? Yes, no, or maybe?
Target Template:
it label ==-1 811 answer answer_choices label] (1 endis
Answer Choices Template:
Yes
|||
No
III Maybe
Prompt from Webson and Pavlick (2021) Input Template:
Suppose it's true that [(premise]] Ihee,, is " "(hhpothesiss) i {("always"),, [["sometimes") or (("never*)) true?
Target Template:
18 1f label 1--1 1) answer_choices [label] 11(8 endif $)
Answer Choices Template:
Always III Never
||| Somet: mes
Prompt from Brown al. (2020) Input Template:
[(premise]
Question: ([hypothesis) True, False, or Neither?
Target Template:
f label !--1 8)1 answer_choices [label] {)(8 endi $)
Answer Choices Template:
True
||| False
|||
Neither
Prompt from Webson and Pavlick (2021) Input Template:
[ipremise]]
Keeping in mind the above text, consider: (hypothessss) Is this
[("always" H,, ["sometimes"" H. or |1 "never"") } correct?
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Target Template:
{8 label
1--1 8)11 answer_choien [label] 11(8 endif 8)
Answer Choices Template:
Always III Never |||
Somet imes
Prompt from Webson and Pavlick (2021) Input Template:
Given [(premise)) Is it guaranteed true that { (hyypothesis) ) "? Yes, no, or maybe?
Target Template:
it Label 1=-1 8114 answer / label] (1(8 endis %
Answer Choices Template:
Yes
||| No |||
Maybe
Input Template:
Given that (premise)) Therefore, it must be true that "[ [[bypothesiss)" !? Yes, no, or maybe?
Target Template:
1f label
!--1 $) inswer_choif
ces ces [1abe1] {)(8 endi $)
Answer Choices Template:
Yes
|||
No
|||
Maybe
Prompt from Webson and Pavlick (2021) Input Template:
Assume it is true that ((premise))
Cherefore, "I "[ibypothesss)" is [["guaranteed") ) [( [("possible") }), or [ ("impossible"]]
Target Template:
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label !=-1
answer_choiccs [1abel] })(8 endif
Answer Choices Template:
Guaranteed |I| impossible |||
Possible
Input Template:
(premisel]
Question: Does this imply that "( "((bppothesis))" Yes, no, or maybe?
Target Template:
[6 if label 1=-1 | [answer__hhiieee [abel])){ endif $
Answer Choices Template:
Yes
|||
No
III
Maybe
Prompt from Williams et (2018) Input Template:
(premisell Using only the above description and what you know about the world, " "[hhppothesis) )" is definitely correct, incorrect, or inconclusive?
Target Template:
label !=-1 $)1 answer_choices (abbell () endis
Answer Choices Template:
Correct
|||
Incorrect |||
Inconclusive
Prompt from Webson and Pavlick (2021)
Input Template:
Given [(premise)) Should we assune that "I (hypothesiss) true? Yes, no, oz maybe?
Target Template:
if label !=-1 $111 answer shoicer [label] () endis $)
Answer Choices Template:
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Yes ||| No
|I|
Maybe
Prompt from Webson and Pavlick (2021)
Input Template:
Cake the following truth: ((prenise))
Then the following statement: ( "((hypothesis)) is (("true))
{("false"ll, or ""inconelusive""
Target Template:
18 1f label 1=-1 $)11 answer_choices [labol] () endis $)
Answer Choices Template:
Prue
||| False |I|
Inconclusive
1.3.2
SUPER_GLUE RTE
Dataset from Dagan al. (2005). Used in evaluation.
Data Example
Key
Value
hypothesis
Weapons of Mass Destruction Found in Iraq.
idx
0
label
1
premise
No Weapons of Mass Destruction Found in Irag Yet.
Prompts
Prompt from Williams al. (2018) Input Template:
Using only the above description and what you know about the world, is "I [hypothesis definitely correct? Yes no?
(premisel)
Target Template:
if label !- -1 1)1 answer_choice [label] () endis $)
Answer Choices Template:
Yes ||| No
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Prompt from Webson and Pavlick (2021) Input Template:
Given ((premise)) Is it guaranteed true that "| ((hypothesis) "? Yes no?
Target Template:
label -1 $)11 answer_choicee [label] () endif
Answer Choices Template:
Yes
|||
No
Prompt from Webson and Pavlick (2021) Input Template:
Suppose (premiss)) Can we infer that "I "[ihpottheiss)1" Yes or no?
Target Template:
[8 1f label != -1 $)1 answer_choicas [label] () endis $)
Answer Choices Template:
Yes
||| No
Prompt from Brown al. (2020) Input Template:
[premise)]
Question: (byypothesis] True or False?
Target Template:
if label 1= -1 $) answcr_cholcos [label] () endis $)
Answer Choices Template:
True ||| False
Input Template:
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(premise))
Question: Does this imply that "[ "[(bypothosis) !*? Yes or no?
Target Template:
[ if label !- -1 $ ccsieer_choices [label]]]
}) }) endif $
Answer Choices Template:
Yes
|||
No
Prompt from Webson and Pavlick (2021) Input Template:
Given ((premise)) Should assume that "[(hypothesis)) " is true? Yes no?
Target Template:
18 label -1 8)11 answer_ choices [label] ()(8 endis $1
Answer Choices Template:
Yes
||| No
Input Template:
Given that (premise)) Does it follow that ((hypothesis) Yes or no?
Target Template:
[6 if label != -1 $) answer_choicas [label] () endi $)
Answer Choices Template:
Yes
||| No
Prompt from Schick and Schütze (2021)
Input Template:
[premise)] Based the previous passage, is it true that no?
( ([hypotheais)
Target Template:
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label -1 8)11 answer_choices [label] {) endif
Answer Choices Template:
Ves
|||
No
Prompt from Webson and Pavlick (2021) Input Template:
{(remiss)] Are we justified saying "((hypothesis) )"? Yes or no?
"?
Target Template:
1f label != -1 $)1 answer_choicss [label] () endis $)
Answer Choices Template:
Yes
||| No
Input Template:
Given that ((premise)) Therefore, it must be true that "[ ([hypothesis)) Ves or no?
Target Template:
label
-1 8)11 answer_cher ces [1abel] ()(8 endi $)
Answer Choices Template:
Yes |||
No
1.3.3 ANLI
Dataset from Nie al. (2020). Used in evaluation.
Data Example
Key
Value
hypothesis label 0
The trolleybue systen has over 2 urban routes
premise
The Parma trolleybus system (Italian: "Rete filovi...
reason
uid
0
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Prompts
Prompt from Williams al. (2018)
Input Template:
(premisse) se)] Using only the above description and what you know about the world, "I (hypothesis) ) is definitely correct, incorrect, or inconclusive?
Target Template:
answer_choices [label]
Answer Choices Template:
Correct
||| Inconclusive
|||
Incorrect
Prompt from Webson and Pavlick (2021) Input Template:
Given ( ((premise)) Should we assume that "I [hypothesiss " true? Yes, no, or maybe?
Target Template:
answer choices [label] ] 1
Answer Choices Template:
Yes
||| Maybe ||| No
Input Template:
Given that (premise)) Docs it follow that { [hypoohhssis] Yes, no, or maybe?
Target Template:
{ answer_choi ces [label]
Answer Choices Template:
Ves
||| Maybe ||]
No
Prompt from Brown al. (2020)
Input Template:
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[premiss)]
Question: ((bypothesis)) Truc, False, or Neither?
Target Template:
{ answer ccoicc_ [label] }
Answer Choices Template:
"rue
||| Neither ||| Palse
Prompt from Schick and Schützee (2021) Input Template:
(premisel) Based the previous passage, is it true that "[ (hypotheaiss) "? yes, no, or maybe?
Target Template:
{ answer_choices [label]
Answer Choices Template:
Yes
|||
Maybe
|||
No
Prompt from Webson and Pavlick (2021)
Input Template:
(premise Are we justified saying that "[hhpottesiss) | Yes, no, or maybe?
Target Template:
if answer_choices [label] }
Answer Choices Template:
Yos
|||
Maybe
I|| No
Prompt from Webson and Pavlick (2021)
Input Template:
Take the following as truth: [(prenise]]
Then the following statement: "I "[ibypothesis) ) is (["true")
[("false" or 1/"irconclusive"]]
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Target Template:
{ answer anawer_choicen choices [label] }
Answer Choices Template:
True
||| Inconclusive III False
Input Template:
ven that ((premise) Therefore, must be true that "[ (hypothesis)) ves, no, or maybe?
Target Template:
[
answer choices label] |
Answer Choices Template:
Yes
||| Maybe I|| No
Prompt from Webson and Pavlick (2021)
Input Template:
Suppose (premise)] Can we infer that "I [[yhpottessss] !? Yes, no, or maybe?
Target Template:
[f anawer_ choi ces [1abel] }
Answer Choices Template:
Yes
|||
Maybe
||]
No
Prompt from Webson and Pavlick (2021) Input Template:
Assume it is true that ((premise))
Cherefore, "I "[ibpptthesss)" is [["guaranteed") ) [( [("possible") }), or [ ("impossible"]
Target Template:
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answer_choices [label]
Answer Choices Template:
Guaranteed
|I| Possible |||
Impossible
Prompt Webson and Pavlick (2021) Input Template:
Suppose it's true that ((premise]] Then, is "(hhypothesis)) i{ [("always"),, {(" [["somtimess")> or (("never")) true?
Target Template:
{ answer_choices [label]
Answer Choices Template:
Always
||| Somot imos
Never
Input Template:
[premise] 1
Question: Does this imply that (( ((hppothesis) )* Yes, no, or maybe?
Target Template:
[aasseer_chiics, [labell}}
Answer Choices Template:
Yes
|||
Maybe
I||
No
Prompt from Webson and Pavlick (2021)
Input Template:
[premisse]
Keeping in mind the above text, consider: ((ypothhssss) Is this
[ [("always"1,, ""sometimes" H1. 1, or |I "never"") } correct?
Target Template:
it answer choices [abbe]] 1
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Answer Choices Template:
Always III Sonstimes
|||
Never
Prompt from Webson and Pavlick (2021)
Input Template:
Based on that information, is the claim: " (hyppthsssss) ( [""tuee)] (("false")), or (("inconclusive"]]
[premiaa)]
Target Template:
answer answer_ choices /nswer_choices/// [aabl]] |
}}
Answer Choices Template:
Prue
|||
Inconclusive
II| False
Prompt from Webson and Pavlick (2021) Input Template:
Given ((prenise] | Is it guaranteed true that {( (hypothesis) |?? Yes, no, or maybe?
Target Template:
[ answer_choicen [label] }
Answer Choices Template:
Ves
||| Maybe ||] No
1.4 PARAPIRRSSE
1.4.1 GLUE MRPC
Dataset from Dolan and Brockett (2005). Used in evaluation.
Data Example
Key
Value
idx
0
label
sentencel
Amrozi accused his brother whom he called the Referring to him as only the witness Amrozi
sentence2
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Prompts
Prompt for the original task intended by the dataset authors Input Template:
:4 if 1abel -- 8)
Paraphrase the following ng sentence: ((sentencel] }
Target Template:
[seeteence2] }
endif }}
Input Template:
want to know whether the following two sentences mean the same thing. [sentencee]]
[sentencc2)]
Do they?
Target Template:
{ answer_choices [label]
Answer Choices Template:
no
||| yes
Input Template:
Does the sentence [[sentencel]]
paraphrase (that is, mean the same thing as) this sentence? [sentencee]]
Target Template:
if anawer choirr__hhhhe ces [ [1abel] }
Answer Choices Template:
yes
Input Template:
Are the fol following two sentences "[ ('"euiivalent "}" or " "not " [sentencel]] [seettence]]
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Target Template:
{ answer choices [
Answer Choices Template:
equivalent
|I|
equivalent
Prompt not for the original task intended by the dataset authors Input Template:
18 if label == 1 8
Generate sentence that means the thing this [seetencel]] }
Target Template:
[Sentencce2 }
(8 endif $
Input Template:
Can replace the sentence [Sentencel]]
with the sentence [Seeteence]]
and have it the thing?
Target Template:
if answer_chor scoperr_choiesss [label] }
Answer Choices Template:
no
||| yes
Input Template:
Do the following two sentences mean the same thing? [ssentencel]
[sentencee2]
Target Template:
answer_choicen [label] }
Answer Choices Template:
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no |1| yes
1.4.2
GLUE QQP
Dataset from Iyer et al. (2017). Used in evaluation.
Data Example
Key
Value
idx
0
label
0
question1
How is the life of math student? Could you descr Which level of prepration is enough for the j...
question2
Prompts
Input Template:
z'm an administrator the website Quora. There are two posts, one that asks "[ [[qqestionn] and another that asks "I [question2]1 J". merge questions they are asking the same thing. Can merge these two questions?
Target Template:
i answer_choices [label] }}
Answer Choices Template:
no ||| yes
Input Template:
[questioon]]
[qqesstio2]]
Pick one: These questions are " pesdupplccttss" or "( ("not dupl icates"))"
Target Template:
answer choices [aabe]] }
Answer Choices Template:
not duplicates II| duplicates
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Input Template:
Are the questions /question]] || and "I [/question2) thing?
)" asking the same
Target Template:
answer choices answer_choices [label]
Answer Choiccs Template:
no
||| yes
Prompt not for the original task intended by the dataset authors Input Template:
Can answer
" "[qqusstoon]) " also be used to answer " [questioo2]] )"?
Target Template:
{
answer_choicse [label]
Answer Choices Template:
yes
Input Template:
Question 1: [qquesiion]]
Question [qquestio2]]
Do these two questions convey the same meaning? Yes or no?
Target Template:
[[answer_choices [label]] }}
Answer Choices Template:
No |||
Yes
Input Template:
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received the questions " ((quuestion)) ) " and "[quusttoo2]) )", Are they duplicates?
Target Template:
enswer_choiee [label] }
Answer Choices Template:
no
yea
1.4.3
LABELED FINAL
Dataset from Zhang et al. (2019). Used in training.
Data Example
Key
Value
label
0
sentencel
In Paris in October 1560 he secretly met the E...
sentence2
In October 1560 he secretly met with the English...
Prompts
Input Template:
Determine 1f the following two sentences paraphrase each other or not. Sent 1: ((sentencel))
Sent [(sentence2)
Target Template:
(aanseer_choieee choices [label] }}
Answer Choices Template:
No
|||
Yes
Input Template:
Sentence 1: [[sentencel] }}
Sentence 2: [[sentence2]
Question: Do Sentence and Sentence express the meaning? Yes No?
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Target Template:
(ansserrchoiice) [1abel]]]
}}}
Answer Choices Template:
No
|||
Yes
Input Template:
(sentencel]]
Is that a paraphrase of the following sentence? [(sentence2]
Target Template:
(answer_choi ces [1abel]] }}
Answer Choices Template:
No
|||
Yes
Input Template:
Sentence 1: [[sentencel]]
Sentence 2: I [senttence]
Question: Can rewrite Sentence Sentence 2?
Target Template:
[annwer__ho cas [abell]]
Answer Choices Template:
No |||
Yes
Input Template:
(sentencel))
Is that paraphrase of the following sentence? [(sentence21]?
Yes or No.
Target Template:
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(answer_choices [label]] }}
Answer Choices Template:
No
|||
Yes
Input Template:
Sentence [senter.cel] }}
Sentence [Senteecce2]
Question: Does Sentence 1 paraphrase Sentence 2? Yes or No?
Target Template:
((anseer_choices [label]} }}
Answer Choices Template:
No
|||
Yes
Prompt for the original task intended by the dataset authors Input Template:
[4 if label -- $)
Paraphrase the sentence:
[sentencee]) )
Target Template:
(sentence2]) }
endii
Input Template:
Sentence [[sentencel]
Sentence 2: [[sentence]]
Question: Does Sentence paraphrase Sentence 22
Target Template:
(aaswer_choiice [label]] }}
Answer
Choices Template:
No
|||
Yes
Input Template:
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Sentence [ssentenccc]
Sentence 2: [sentencc2]}
Question: Do Sentence and Sentence express the same meaning?
Target Template:
[aanseer_chocess [labell]]
Answer Choices Template:
No
|I| Yes
Prompt from Brown al. (2020) Input Template:
(sentencel)) Question: ((sentence2)) True or False?
Target Template:
answwrr choices [label]]
Answer Choices Template:
False
||| True
Input Template:
Sentence 1: [sentenceel] }
Sentence 2: [sentencee)
Question: Can rewrite Sentence Sentence Yes No?
Target Template:
(aasswer_choicss [label]]} }}
Answer Choices Template:
No
|||
Yes
Prompt from Brown al. (2020) Input Template:
{ [Sentencee)) Question: ([sentence2)) Paraph rase not?
Target Template:
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(answor_choices [label]]
}}
Answer Choices Template:
No |||
Yes
1.5
QA CLOSED BOOK
1.5.1
AI2_ARC ARC-CHALLENGE
Dataset from Clark et (2018). Used in evaluation.
Data Example
Key
Value
answerKey choices id
A { ('label'; ['A', 'B', 'c', 'D'), 'text'; ['dry palm... Mercury_sc_4151 George wants to warm his hands quickly by rubbing
question
Prompts
Prompt not for the original task intended by the datasct authors Input Template:
Pick and copy all the incorrect options for the following question:
[[question]]
Options:
((choices| ]
join ("\n= ")
Target Template:
{8 for in range (choices ("label") |length) 8)
[ if 1 != choices ["label"] index (answerKey) {)
((choices ] [1]1)
endif $1
endfor $)
Input Template:
Here's s problem solve: [questtion)
Among the following options: which is the correct answer? it for letter, t in zip (answer_ choices, sooices.text) $1 ([letter)]: [[t]]
(8 endfor $1
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Target Template:
((answereey)) }
Answer Choices Template:
A 111 B 111 c III
Input Template:
{ [qqustionn]
Options:
hanswer_choicee join ("\n= ("inn")n- "}
Target Template:
(answer_ (aanseerchhiiess choices (choices ("label") .indxx (answerKey) }1}
Answer Choices Template:
[choices.tes text join (" ("111")
Input Template:
hesitating between 4 options to answer the following question, which option should I choose? Question: [(question) Possibilities: [[aasser_choicess
join ("\n- "}]}
Target Template:
([answer_choif ces [choices [* ("label" "] index (answerKey) }}}
Answer Choices Template:
f(choices.ter text join ("111"))
Input Template:
gave my students this multiple choice question: { (questioon)
Only one answer is correct among these 4 choices: [[answer_choicee join ")
Could you tell me which one correct?
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Target Template:
(answer choices choices ("label") .indxx (answerKey) 11
Answer Choices Template:
[ccoiice. text join (1
Input Template:
Pick the most correct option answer the following question.
[qqusstion]]
Options:
18 for letter, in zip (Aplanwwe__choiee choices.texts $1 ((letter] 1: [[t]]
endfor $1
Target Template:
(answerKey]] }
Answer Choices Template:
III B III
1.5.2 A12 ARC ARC-EASY
Dataset from Clark et al. (2018). Used in evaluation.
Data Example
Key
Value
answerKey
B
choices
('label': ['A', 'B', 'C', 'D',, 'text': ['a leg mu...
Mercury_72200
question
Which factor will most likely cause a person to de...
Prompts
Input Template:
Pick the most correct option to answer the following question.
[[question]]
Options:
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for letter, zip (answer__choicss choices text) $} ((letter]] [[t)) endfor $1
Target Template:
(answerKey)) }
Answer Choices Template:
A 111 B B 1II
Input Template:
[(question]]
Options:
[answer_choices join (")n= ")
Target Template:
(ansver [fanseer_choices| choiccs ("label") .indxx (answerKey) 1
Answer Choices Template:
[(choices. join ("}]
Input Template:
am hesitating between 4 options to answer the following question, which option should I choose?
Question: [(question) Fossibilities: {[answer_choices
join n")n- "}]}
Target Template:
[answee__coices (choices ("label") index (answerKey) }}
Answer Choices Template:
( [choices.tess text join (* (1)")))))))) }}
Input Template:
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gave my students this multiple choice question: ((question)
Only one answer is correct among these 4 choices: (fanswer_choices join ("\n- "}]}
Could you tell me which one is correct?
Target Template:
(aasser__hoiiee choices (choices ("label") "] .index (answerKey) }
Answer Choices Template:
{
[choices.text join (" ("11)"))
Prompt for the original task intended by the dataset authors Input Template:
Pick and copy all the incorrect options for the following question:
(qquesiion)>
Options:
((choices ("textt)
join ("An- }}
Target Template:
for in range (choices ["label" [lenghh) $) 18 choices ["label"] iindex (answerKey) 8)
{(choices ("text") [}]}}
18 Sndif $} endfor $}
Input Template:
Here's problem to solve: ((question))
Among the following options, which is the correct answer? 18 for letter, t in zip (aaaaaaanahoiceeeeeeeeeeaeeeeeee choices.text) $] [[letter]]: [[t))
{8 endfor $)
Target Template:
aanswereey
Answer Choices Template:
A
|||
111 C 111
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1.5.3 KILT_TASKS ΙOOPOOTQ
Dataset from ?. Used in training.
Data Example
Key id input meta
Value
0770099554290196a1055
which magazine was started first Arthur's Magazine... ("left_context's "', 'mention'; "' fiihht_cottxt..... [('answer': "Arthur's Magazine", 'meta': s'ccrre :....
oulput
Prompts
Prompt not for the original task intended by the datasct authors Input Template:
:8 if output $}
Here's complex questi on that requi res someone about the input, can you answer it? [input]]]
Target Template:
!(output I map (attraatte=""aaseeeaaa | list choice! (4 endif $}
}
Prompt for the original task intended by the dataset authors Input Template:
18 it output 8
Combine facts and answer this: (Linput]
Target Template:
(output | map (attribute "antter"") 18 endif
list choice))
Prompt for the original task intended by the dataset authors Input Template:
13 if output $
Formulate answer to this elaborate question: ((input))
Target Template:
((output map
1ist
choice!
18 endi:
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Prompt not for the original task intended by the dataset authors Input Template:
:8 if output $)
FINAL EXAM
Question ([input))
Target Template:
(output | map "answerr) endif
list choice)
Prompt not for the original task intended by the dataset authors Input Template:
it output
[inputt]]
Target Template:
(output | 16 endif $}
map
list choice) }
1.5.4 TRIVIA UNFILTERED
Dataset from Joshi al. (2017). Used in evaluation.
Data Example
Key
Value
question
Who was President when the first Peanuts cartoon W...
question
tc_0
question source
httt:://ww.triviaccorttet com/
entity pages
"ooc_ssurce': I'TagMe'], 'Hilename': ['Peanuts.tx... d'decription'; I'Feanuts 1950s. The first Feanuts... 'aliases': l'Presidency Harry S. Truman', 'Har...
search_results
answer
Prompts
Prompt not for the original task intended by the datasct authors Input Template:
+ if anawer a alliasse iases $
Guess question that has the answer " "[(anseer.al al iases ccoicee) )*
Target Template:
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[questioon] ( endif 8}
Input Template:
The goal is to predict an English answer string for an input English question.
Question (Iquestion)
Answer
Target Template:
{8 if answer aliases 8 aaasser.aliases choice))
endif $)
Input Template:
Answer the following question. [iquestion]]
Target Template:
18 answer fnswer.aliste aliases $
(answer, aliases choice)
endif $}
Input Template:
I've always wondered: ( [(question]]
Target Template:
18 if beswer.aliasee $ aenawer.aliasee choice) endif $)
Input Template:
Question [(question)] Answer
Target Template:
it inswer.aliase { faanserrallases choice)
endif $1
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1.5.5 WEB_QUESTIONS
Dataset from Berant al. (2013). Used in evaluation.
Data Example
Key
Value
answers question url
I'Jazmyn Bieber', 'Jaxon Bieber'] what is the name of justin bieber brother?
http://wwnt freebase. www.eaaee...m/iieeeeeeeeeeeeeeee httpe//ccceeeee//////sst..n.niaa bieber
Prompts
Input Template:
Give the correct facts
answer this: (question))
Target Template:
(answers
choice]
Input Template:
Give possible correct the question "[[ question }
Target Template:
{ answers choics
}
Input Template:
What the answer that question: [quest on
Target Template:
(annwers
choice))
Input Template:
Short general knowledge question: [qquestion)
Target Template:
(answers
choice))
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Input Template:
{
question cap.taliia }
Target Template:
[ answers
choice
}
1.5.6
WIKI QA
Dataset from Yi et al. (2015). Used in training.
Data Example
Key
Value
answer
African inmigration the United States refers to...
document title
African immigration to the United States
label
0
question
HOW AFRICAN AVERICANS MERE IMMIGRATED TO THE US
question_id
Q0
Prompts
Input Template:
Question [[question]]? } Would "((answer))" be reasonable answer?
Target Template:
answer_chot ces [1abe]] }
Answer Choices Template:
No
|||
Yes
Input Template:
am verifying the answers generated by an automatic ic system to the following question: liquestion] Suggested answer: Hansweri] Should validate this answer?
Target Template:
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(answer_choices [label]} }}
Answer Choices Template:
No
|||
Yes
Prompt not for the original task intended by the datasct authors Input Template:
1abel what is the question on to: "[ (anawer) "? The topic is ([document_title)))
Target Template:
"( [[questionl J
[8 endii $1
Prompt not for the original task intended by the dataset authors Input Template:
{8 label 8)
Determine the topic of the quest guestion-answer pair.
Question: "[[question] } Anawer: "I "[[answer]]" Topic:
Target Template:
diocument_titiee endii $1
Prompt for the original task intended by dataset authors Input Template:
18 if label -- })
Generate question about the topic "I (ddcument_tittee) whose answer would be: [[answer)).
Target Template:
[[question] !? 18 endif $
Input Template:
Question: [qqeesiin]]
found the following answer Google: [answer)
Is that a correct answer? Yes or no.
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Target Template:
(aansee__hooie ces [1abel]]
}
Answer Choices Template:
No
|1| Yes
Prompt for the original task intended by the dataset authors Input Template:
18 if label -- 8]
Determine the topic of the question. Question: "[[question]]?" Copic:
Target Template:
[[docmmet__itles 18 endif $}
Input Template:
The exercise is to decide whether the question accepts the proposed suggestion correct answer. IE yes, write [[answer_chhiiee otherwise write [| Question: ((question)) Suggestion: [[answer]]
[1]]
Target Template:
(answer_choicos [label]] }
Answer Choices Template:
False
|||
True
Input Template:
This is a correct answer to the following question about [(documet__tttlel) ]. Yes or no?
Answer: ((answer))
Question: ([question)
Target Template:
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(answor_choices [label]} }}
Answer Choices Template:
No
|||
Yes
Prompt for the original task intended by the dataset authors Input Template:
16 if label == 1 )
Determine the topic of the passage. [[answer))) "opic:
Target Template:
[document_cittal 16 endif $}
Input Template:
16 it label Answer this question: (iquestion) 1?
Target Template:
[answer] } 18 endif $
1.6 QA EXTRACTIVE
1.6.1 ADVERSARIAL.QA DBIDAF
Dataset from Bartolo et al. (2020). Used in training.
Data Example
Key
Value
title
Yale_University
context
Slack (2003) compares three groups that conducted
question
what year were the research groups compared
answers
l'tett:: ['2003'], 'answer_start'' [7]]
metadata
('split': 'train', 'model iodel__i__theloopp 'BiDAF')
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Prompts
Input Template:
18 if metadata. split !- "test" &}
extract the answer to the question from the fol lowing context. Question: [qquestion]
Context: [fcontext])
Target Template:
(fanswera.tem) choice)
endif
Input Template:
18 it metadata.split != "test" $}
Given the following passage
"[(context))",
answez the following question. Note that the answer is present within the text.
Question: ((question))
Target Template:
(answers.
choice)
[6 endi1
Prompt not for the original task intended by the dataset authors Input Template:
want test the ability of students to read passage and questions about it. Could you please come up with good question for the passage "[[context))"?
Target Template:
[qquestion)
Input Template:
:8 vetadata.sp1ii !- "test" $)
know that the answer to the question "[qquestion]>" }" is in [ccottexi]" I.". Can you tell me what it is?
Target Template:
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(answers. choice) & endif 8)
Input Template:
18 if metadata, splitt split != "test" 8}
Question: "I [[question]]] )*
Context: "[ [[context:]
Answer:
Target Template:
(answers.. text choice) } (8 endif $1
1.6.2
ADVERSARIAL. QA DBERT
Dataset from Bartolo al. (2020). Used training.
Data Example
Key
Value
dab0 7ed8d fa2d861 Babc3a477a4edffo 4a4 Empiricism
title
context
A generation later, the Irish Anglican bishop, Geo... what concept is mentioned last?
question
answers
!'text:: ('subjective idealism'), 1, 'answer_start" ''split'; 'train', "model_in_the_logp 'BERT-Lazg...
metadata
Prompts
Prompt for the original task intended by the dataset authors Input Template:
want to test the ability of students to read a passage and answer questions about it. Could you please come up with a good question for the passage "[[context]]"?
Target Template:
[[question] |
Input Template:
:8 it aataaata.sspie != "test" $1
know that the to the question "[question]] !" is in [[Cooteett] I!" Can you tell me what is?
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Target Template:
(answers. text choice]. { endif $}
Input Template:
split 16 it metadata. split != "test" $1 Question: "I [qqusstion)
Context:
"[[context)] )"
Answer:
Target Template:
(answers text choice) endif $}
Input Template:
{4 if metadata, metadata.split !- "test" $}
Extract the answer to the question from the following context. Question: [(question]
Context: ([context))
Target Template:
(answers. text choice) }
endif
Input Template:
18 metadata.split != "test" $1
Given the following passage
"[(context))",
answer the following question. Note that the answer present within the text.
Question: ((question))
Target Template:
(answers. text text choice) }
13 endif $)
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1.6.3 ADVERSARIAL.QA DROBERTA
Dataset from Bartolo al. (2020). Used in training.
Data Example
Key
Value
id
title
Napoleon
context
When he became First Consui and later Emperor, Nap... What jewelry like accessories did he wear?
question
answers
l'text': ("Légion d'honneur star, medal and ribbon...
metadata
('split'; 'train',
'RoBERTa-L...
Prompts
Prompt not for the original task intended by the dataset authors Input Template:
want to test the ability of students to read a passage and answer questions about it. Could you please come up with good question for the passage "I "[[ontext]]
Target Template:
[(question))
Input Template:
18 if metadata.split != "test" 8)
know that the answer to the question "[qquetion]] }" is in [[context])": Can you tell what it is?
Target Template:
(answers. choice)] endif $)
Input Template:
18 metadata. split != "test" $1
Question: "I [qqusstion]] )*
Context:
"([context) )
Answem:
Target Template:
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(answers. choice) $ endif 8)
Input Template:
[8 it metadata.spli != "test" $
Extract the answer to the question from the following context. Question: [Iquestion]] Context: ((context)]
Target Template:
(answers. choice)) endif
Input Template:
18 if splitt != "test" $}
Given the following passage
"[[context))",
answez the following question. Note that the answer is present within the text.
Question: ((question))
Target Template:
[lanswers.tew) text choice) {8 endif $1
1.6.4 DUORC SELERC
Dataset from Saha al. (2018). Used in training.
Data Example
Key
Value
answers
'They arrived by train.
no_answer
False
plot
200 years in the future, Mars has been colonized b... /m/03vyhn
plot_d
question
did police arrive at the Mars mining camp? -aaeadd-g3f--1--aac--- 4bddE99500
question_id
title
Ghosts of Mars
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Prompts
Prompt for the original task intended by the dataset authors Input Template:
1 if no_answer -- fa sels
Generate question that has the following answer: [anssera ccoicce] for the following movie plot: [plot]
Target Template:
[qquestion] on]] 16 endii
Input Template:
movie director and just received the following movie plot. Could you help mo answer this question? not, let me know by writing "( (("No answerable"))".
Plot title: ([title))
Movie plot: []]lo]]] My question: [qquestion]]
Target Template:
18 no answer } Not answerable 18 else $1
(answers)
choice] choicel) endi
Input Template:
Extract the answer the following question from the movie plot. If the question ian't answerable, please output "[["Can' 't answer") Question: [qqusstion]]
Citle: [ititle]]
Movie plot: [[plot]]
Target Template:
18 if no_answer 8)
Can't answer (6 else $1
(answers choice } endi $}
Prompt not for the original task intended by the datasct authors Input Template:
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Generate question about the following movic plot: (1 plot )
Target Template:
{
question
Input Template:
Please answer the following question about this movie plot. If it's un-answerable, please output "[[ "No answer"))*.
Question: ((question). Movie plot title: [ititle]]
Movie plot: [[plot]]
Target Template:
18 if no_answer }
No answer
else $1
(answers choice } endif $}
Prompt not for the original task intended by the dataset authors Input Template:
18 it no_answer == falset)
Build movie plot around this:
question ((answers ccoicee
Target Template:
[6 plot 11
18 endii
Input Template:
Question: [questionn]
If there is no answer, please output "[("Isuuficiien information to provide an answer. ")]" Movie title: [[title)) Context: ([plot))
Target Template:
18 no no_answer 3)
Insufficient information to provide an answer.
18 else $1
| endii
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Prompt not for the original task intended by the dataset authors Input Template:
Suggest movie title for the following movie plot: (Iplot
Target Template:
[tiitee))
Input Template:
trying to decide whether it's worth it invest in this film proposal. Can you help say "["No can't")]"
few questions? If you can't, please
Question: on: [(question) Movie title: [[title]] Movie plot: []plot])
Target Template:
[4 if no_answer }
No can't
{8 else $1
(answera|
{8 endif $)
1.6.5 DUORC PARAPIRASERC
Dataset from Saha et al. (2018). Used in training.
Data Example
Key
Value
second command Sergeant Jeri cho and pri scner... False
no answer plot
Set in the second half of the 22nd century, Mars h... /m/03vyhn
plot id
question
is there with Melanie Ballard? -aw--ld------ac---------------e-
question
title
Chosts of Mars
Prompts
Prompt not for the original task intended by the dataset authors Input Template:
18 it answer falset)
Build movie plot around this: question 1)
((answers cchicee) )
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Target Template:
i{ plot }} :8 endif $}
Input Template:
am trying to decide whether it's worth to invest in this film proposal. Can you help me answer a few questions? If you can't, please say "["NNo can't can't"))".
Question: ([question]) Movie title: [[title)] Movie plot: [[plot))
Target Template:
[8 no_answer }
No can't
{ else $}
(answers (coicee)
endif
Input Template:
Question: (qquestion))
If there is no answer, please output "[I "If"Inuufficien information to provide an "nswer.")"" Movie title: ([title)]
Context: ((plot))
Target Template:
18 if no_answer 8)
nsufficient information to provide answer. else $} (answers choicee)
endif $1
Input Template:
am a movie director and just received the following movie plot.
Could you help mo answer this question? If not, let me know by writing "(["Not answorablo"))".
Plot title: [[title)] Movie plot: ((plot)) My question: [[question)]
Target Template:
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:8 no_answer $
Not answerable
18 clse $
(answera) choicee)
endif
Prompt for the original task intended by dataset authors Input Template:
Generate question about the following movic plot: (1 plot ]
Target Template:
i6 question
Input Template:
Extract the the following question from the movie plot. If the question isn't answerable, please output "[1 "("Can'' 't answer") Question: [[question))
Citle: [[title)]
Movie plot: [pplot]
Target Template:
18 no_answer $)
Can't answer
{8 else &}
(answers choice }} endi
Prompt not the original task intended by dataset authors Input Template:
Suggest
movie title for the following movie plot: (pplo:]
Target Template:
[(title)) }
Input Template:
Please anawer the following question about this movie plot. it's un un-answerable, please output "( "N"N answer")]*.
Question: [qquestion]] Movie plot title: [[title)]
Movie plot: [[plot])
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Target Template:
18 if
No answer
(6 clac $}
(answers choice }}
16 endif $}
Prompt not for the original task intended by the dataset authors Input Template:
18 if no answer == false8)
Generate a question that has the following answer: [[answess/choice))
for the following movie plot: (plot]]
Target Template:
[qquestion] {8 endi $}
1.6.6 ROPES
Dataset from Lin al. (2019). Used training.
Data Example
Key
Value
answers
"text': ('cup B']]
background
Passive transport occurs when à substance passes t... 1971664873
id question
which cup has higher concentration of sugar? put two cups, cup and cup B, filled with e...
situation
Prompts
Input Template:
18 if answers.text 8)
Please answer correctly the following question related to the paragraph below.
11 question }}
1( situation }
Hint: (( background })
Target Template:
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answers. text choice endif 8)
Prompt for the original task intended by the dataset authors Input Template:
answers. +]
if situation }}
Given the paragraph: above, please answer correctly the following question:
{ question }
Target Template:
mnswers.text
choice }
endi:
Input Template:
18 if anawers text }
Background: {{ background }}
Paragzaph: [I situation }}
Given the paragraph: above, please answer correctly the following question: ({ question }}
Target Template:
{ answers.tox. choice }}
endif $}
Input Template:
18 mnawers.text $)
Given the background: ((background))
and the situation: [[situation]
Answer the following question: ([question)
Target Template:
answers.tew. choice ! endif $}
Prompt for the original task intended by the dataset authors Input Template:
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answers. text 8 { situation 1]
{{ question }}
Target Template:
answers.text endi1
choice
Input Template:
18 nnowers.text t)
(f situation 1]
14 question }}
Hint: {{ background }}
Target Template:
answers. text endif $}
choice)
Input Template:
[8 if anawers text $)
{{ background }}
{f situation }}
question }}
Target Template:
answers.tem text endif
choice
Input Template:
18 answers. text { can use this background: (backkgroudd)
Now, I have a new situation: ((situation)
Answer this question please: [(question]
Target Template:
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answers. text endif 8)
choice
Input Template:
16 it answers.text { You are given new situation: ([situation)
and a hint ([background))
Please answer this question ((question))
Target Template:
endif choice }
Input Template:
{8 if anawers. text 8) have new situation: ( ((situation))
But can use this background: ((backgrourd)
what is an answer for this question: [[question}}
Target Template:
answers. text choice !] endif $)
Input Template:
answers.text $)
if situation }}
Given the paragraph above, please answer correctly the following question:
1f question
}]
Hint: {{ background }
Target Template:
[f answers. text
choice }
18 endif $)
Input Template:
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:8 answers. text 8
read this background article the other day; (bbckkgrondd) )
I am facing a sitcation today: ((situation))
Using the knowledge I acquired from the background article, how should answez correctly the following question regarding my new situation: (quustiion|
Target Template:
[f answers. choice }
16
endi1
1.6.7 SQUAD
Dataset from Rajpurkar et al. (2016). Used in evaluation.
Data Example
Key
Value
(ace85543aeaaa14000 Beyoncé
title
context
Beyoncé Giselle Knowles-Carter (/bijnse/ bee-Y...
question
When did Beyonce start becoming popular?
answers
('text': I'in the 1ate 1990s'], 'answer_start's [2...
Prompts
Input Template:
[8 set seq "Answer the question depending on the context.', "What is the answer?',
i1 seq choice }}
Context: [(context]] ) Question: [(question]] Answer:
Target Template:
18 if anawers text Answer not in context 16 else $1
tex [0]]] endif $1
Prompt not for the original task intended by the datasct authors Input Template:
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:8 answers text [] Determine the question that you might have asked to get back the following answer for the given context
Context: { ([context));
Answer: (ansserss.. text [0] Question:
Target Template:
[qquestion] I
{8 endif $
Prompt not for the original task intended by the datasct authors Input Template:
{8 set seq [
"What is this about?
'What is the paragraph about? '
"Get the topic from:
'From the passage, get the topic', 'I want to know the topic. ',
'Topic from the passage:
'Topic from the paragraph: '
seq choice }} [context]]
Target Template:
i(title
replace ("_", "}
Prompt not for the original task intended by the dataset authors Input Template:
{8 set seq [
"his is about '
'what ia this about?
'The paragraph is about "Watt the paragraph: about? ' "Get the topic: ',
"From the passage, the topic is', 'I want to know the topic. ' 'Topic from the passage: 'Topic from the paragraph: '
8 [context]]
seq choice }}
Target Template:
i[title replace ("_", ")1l
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Prompt not for the original task intended by the dataset authors Input Template:
18 anawers text []
What is a question that would give the following answer? Answer: [aanswrrs.text 11)
Question:
Target Template:
[qquestion] endi &}
Input Template:
:8 set seq [ *Can you tell
*leaae tell
"ell me
From the passage,
"I want to know ' want to ask ' 'Wat the answer to: 'Find the answer to: 'Answer: ',
[context]] {{ seq choice }} [[qqustiion)
Target Template:
(8 if anawers. text == [1 8)
Answer not in context
18 clse $1
annwerrs.tok [
endif $1
Input Template:
16 set seq 'Answer the question depending on the context.', 'What is the answer?',
: 8]
if seq choice }} Context: [[contet]]];
Question: [[question]
If you can't find the answer, please respond "unanswerable". Answer:
Target Template:
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:8 answers. []
unanswcrable
18 clse $
(answera.text [0]]]
endif
Prompt not for the original task intended by the dataset authors Input Template:
{8 if answers.text text != []] 8
[question]]
Target Template:
((nnswers. text [0])]
[6 endi1 $1
Input Template:
18 set seq [ 'Can you tell me *Please tell 'Tell me
"From the passage, ' want to know ' "I want to ask ' "wht the answer 'iind the answer to: Ansser: ',
[context]] II seq choice }] ([question)) If you can't find the answer, please respond "unanswerable",
Target Template:
18 it answers.text text == 1] $1 unanswerable
else $)
mnaserss.ee. [0]]]
endif $1
Prompt for the original task intended by the dataset authors Input Template:
Context:
((context)) );
Question: { [[question]]
this question answerable?
Target Template:
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answers text
[]
(answer_choices [0]))
$ clse $
(answer_choicas, cas [1])}
endi
Answer Choices Template:
yes
||| no
Prompt not for the original task intended by the dataset authors Input Template:
:8 set seq
'Determine the topic the guestion-answer pair. ', "Find the topic.
'What is the topic from this?
$}
18 answers.text
1f seq choice }}
Question: ((question)), ; Answer: teenswers.text [0])};
Topic:
Target Template:
[titte]]]
endif
Prompt for the original task intended by dataset authors Input Template:
What is the following passage about? ? [context]]
Target Template:
M(title replace ("_", "}}}
1.6.8
SUPER_GLUE RECORD
Dataset from Zhang et al. (2018). Used in evaluation.
Data Example
Prompts
Input Template:
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Key
Value
answers
['Nuria']
entities
''Afghanistan', 'Badam Bagh', 'Mariam', 'Nuria'] paasaago'; 0, 'query': 0}
idx
passage query
The harrowing stories of women and children locked...
The baby she gave birth to is her husbands and he
passage }}
{ query }}
Which one is the "I "/["@placeholder"]1 || entities join(", ") 113
Target Template:
(6 1f ( answers
| length > 0.. $} {{ answers
choice }
{8 endif $)
Answer Choices Template:
{ entities join (" F1
Input Template:
The following document has been corrupted. Tell me what "( ((""ppaaceholder")) is referring to.
Document: {{ passage }} {f query }}
Target Template:
( answers length &) answers choice }
18 ondif $}
Answer Choices Template:
i entities join (" (11") 11
Input Template:
Summary:
passage.split (*reghhhghh"""""""ghhhhh"""" [1:] join ("An- ")
Article:
passage. split ("@highlight") [0] 11
Target Template:
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Now that you've read the article, please write sentence to add to Published as a conference paper at ICLR 2022
( answers length > 8) query replacc ("éplaccholder", answers choice) }} 1 endif $}
Answer Choices Template:
16 for entity in entities[:-1] $1 query replace ("éplaceholder", entity) 11 1II 18 endfor (f query replace ("éplaceholder", entities(-1]) }}
Input Template:
Summary:
((
[1:] ("An= ") HF
Article:
if passage.splis ("@highlight") (0) F]
Target Template:
18 ( anawers length > ({ query repl ace ("êplaceho) der", answers choice) 1) 18 endif $1
Answer Choices Template:
18 for entity in entities [-11] $1 query replace ("éplaceholder", entity) 11 III 18 endfor {f query replace ("@placeholder", entities (-11) 11
Input Template:
passage }} query }}
You should decide what "(("@placeholder)) is referring to. Choose
between: ((answer_choicas
join n")n- "}}}
Target Template:
18 it ( answers length $ answers choice ||
i8 endit $1
Answer Choices Template:
entities
join (" (11") 11
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Input Template:
{ massage.splic ("t.ihthhhhhtthtieeee ("hhighlight [o] }}
Summary:
(( (Megagggg"pp""""""""eeoogg"""""" [1:] join ("An- ") |1
Target Template:
18 if ( answers length > 8)- {{ query replace ("éplaceholdee
answers choice) 1) 18 endif $
Answer Choices Template:
16 for entity in entitics(:-1] $} If query replace ("éplaceholder", entity) HF III {& endfor $1 (f query replace ("éplaccholder", entities (-11)
Prompt not for the original task intended by the datasct authors Input Template:
Article:
{6 salstge.split ("r ("@highlight") [0] }}
Highlights:
splst [1:] foin(")n")
Target Template:
18 if ( answers length > 8H query replace ("éplaceholder", answers choice) 1) {8 endif $
Answer Choices Template:
18 for entity in (ttities[=-1] $1 [I query I replace ("éplaccholder", entity) 11 III 18 endfor (( query replace ("@placeholder", entitiea [-1]) }}
Input Template:
[f passage 1)
if query }}
In the queation above, the "( "(""pplaceholder" stands for
Target Template:
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answers
length
answers
choice 11($ endif 8
Answer Choices Template:
{ entities join (* (11") }}
Input Template:
After zeading the article, write another sentence to add to it. passage replace ("@highlight", "An- }
Target Template:
18 ( answers length > {){ query replace ("éplaceholder", answers choice) 1)18 endif $}
Answer Choices Template:
18 for entity [n=itie[[-1]] $) {{ query | replace ("éplaceholder entity) }1 III {8 endfor 8) {f query replace ("Oplaceholder", entities [-1]) }}
Input Template:
Please read the following news article and write another sentence to add to 1t.
if passage replace ("@highlight", "An- }}
Target Template:
18 if ( anawers length > 8) (( query replace ("êplacshol der"" answers choice) }} {$ endif $}
Answer Choices Template:
18 for entity entities[:-1] 8) {{ query entity) H1 III 18 endfor $) {f query replace (" ("@placeholder", entities[-1n 11
| replace ("@placeholdes
Input Template:
[6 passage }} if query }}
What could the "[["@placeholdes
acehol acehol bs? {{ entities join (", ") }}?
Target Template:
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( answers
length
answers
choice 11($ endif 8
Answer Choices Template:
{ entities
join (" [11)) F
Input Template:
passage }}
query H}
am trying to decide what "( ((""placchooderr) ) means in the previous text.
Help by choosing ng option between:
{f entities join n"\n- ") }}
Target Template:
{ answers length > 8}
{ answers choice }
endif $1
Answer Choices Template:
:enttities
join (" [1))
Input Template:
16 passage 1)
1f query 11 Here, the placeholder refers
Target Template:
if ( answers
length 0 {){{ answers choice }
endif
Answer Choices Template:
{ entities join (" 111") 1
Input Template:
[1 passago.splie ("@highlight") [0] }}
Highlighta:
passage. split ("#highLight") [1:]] join ("An- ") HI
Please write an additional highlight.
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Target Template:
:8 ( answers length 8)- {f query replace ("éplacsholder",
answers choice) }} { endif $}
Answer Choices Template:
for entity entities[:-]] $} {f query replace ace ("@pl aceho ler"", entity) }} III (8 endfor 8) {( query replace ("êplaceho der",
entitiesl-1]) 11
Input Template:
Exercise:
Extract from the text the correct entity that "(""ppaacehodder"" j is zeferring to.
passage }}
query }}
Target Template:
if ( answers length > 0 8} answers choice } endif
Answer Choices Template:
!enttities join (" ("111"))
Input Template:
{1 passage }} query }}
Pick one option, "1""plaaeholder"") refers to: alanswercchoiees join ("An- "}]}
Target Template:
{ answers length $1
{ answers choice }]
endi $}
Answer Choices Template:
(entities
join 11)1
Input Template:
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passage
replace ("@highlight", "An- ") }}
Target Template:
18 ( answers length > 81- {f query replace ("éplaccholder", answers choice) }} 1 endif $}
Answer Choices Template:
18 for entity in ertities[=-]] &) {f query replace ("êpl aceho lero", entity) 1F III endfor query replace ("@placeholder", entitiesl-11) 11
Prompt for the original task intended by the datasct authors Input Template:
Article:
16 apssage.spiit ("thighlight"") [0] }}
Highlights:
{{ passage split "" join (*An- ")
Target Template:
18 it ( answers length > 0 $1- |I query replace ("éplaceholder",
answers choice) 18 endif 1
Answer Choices Template:
18 for entity [==] 8) {{ query replace ("êpl aceho der", entity) 1F III 18 endfor 8) {f query replace ("éplaceholder", entities [-1]) (-11) 1]
Input Template:
[1 passage 1)
if query }}
Can you figure out what does the "[ "(("@pacceholder" mean? It means
Target Template:
if anawers
1ength
0 {{ answers
choi ce })) endif $)
Answer Choices Template:
entities
join (" (11)) 11
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Input Template:
passage replace (ealace""@higee) ("thighlight, "\n") }}
Target Template:
18 it ( answers length > 8H1 query replace ("épLaceholder", answers choice) 1) 18 endit %
Answer Choices Template:
18 for entity entities [:-1] 8) {f query | replace ("éplaceholder", entity) HF III 1$ endfor 8 (f quary replace ("éplaceholder", antitics(-1]) }}
1.6.9 QUOREF
Dataset from Dasigi et al. (2019). Used in training.
Data Example
Key
Value
answers
a'aswer_startt [250], 'text'; ['Catherine']
context
The carthquake swarm was noted on October 12, 2007...
id
question
what is the first
the person who doubted i
2007 (2008 Nazko earthquakes https: /en. wi ki
url
Prompts
Input Template:
The anawer to the question: { ((question) inside the article: [context)), can you guess it
Target Template:
(annwerrs
choice)
Input Template:
Given the following context:
[ccontext]] H
answer the following question:
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( (question)]
Target Template:
(answers. text
choice)}
Input Template:
The fol fowlowng article contains an answer for the questions { ((question) ion)
can you please find it?
[context]
Target Template:
(answers.. text
choice)
Input Template:
This article: [context]] } contains an answer for the question: [question]], what is it
Target Template:
fannserrs. choice)
Input Template:
[qquetiion]
Answer the above question based on the context below:
[conteet]] }}
Target Template:
(answers. text
choice: ce
Input Template:
What is the answer for the question: { [question]] from the following article ?
[(context]]
Target Template:
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(answers.
choice)
Input Template:
where I given the following article, what is for the question: [questioon
have
[context]]
Target Template:
(lanswers.
choice) }
Prompt for the original intended by the dataset authors Input Template:
Given the below context:
[[contet]]]
Guess valid title for it!
Target Template:
[(title))
Input Template:
Found the following article online, it to answer the question: [question]]
[context]] }}
Target Template:
(answers. text
choice)}
Input Template:
friend aaked me to answer this question: [iquestion]] ), using the article: [[contet]),, what would be the answer ?
Target Template:
( aaswerrs text text
choice) }
Input Template:
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Read the following paragraph and extract the answer the question: [[question]]
[context]]
Target Template:
answers.tea
choice) )
1.7 QA MULTIPLE CHoICE
1.7.1 COS
Dataset from ?. Used in training.
Data Example
Key
Value
webnath is designed to help you solve math problem
choices
I'par'', 'coloring book', 'garden center', 'math p... "there are 10 apples on an apple tree. three fall
extractive expelaaatteionniiiooonnnn
id question
"There 10 apples apple Three fall...
Prompts
Input Template:
if question 11 Choose the most suitable option to answer the above question. Options: {{ answor_choicos
join ("An- ") }}
Target Template:
i( answer }}
Answer Choices Template:
{ choices
join (1111)
Input Template:
if question }}
Choose the most suitable option to answer the above question. Options i8 for in range (choices
Length)
107

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join [lanswer_choloes [k], choices [k]]) endfor $
Target Template:
answer choices snswer_choices/ (choices.. index (answer) ]
1
Answer Choices Template:
Prompt not for the original task intended by the dataset authors Input Template:
Question: ((question)
Choices:
choices join ("An-
The rationale to choose (answer)) ) as the answer is that:
Target Template:
Input Template:
question 11
{{ enawer_choiee join ("An= ") }}
The best answer is
Target Template:
if answer }}
Answer Choices Template:
choices
join (p11("
Prompt for the original task intended by the dataset authors Input Template:
Here's question and few possible answers:
Q: {f question }}
Possible A: II choices joint" ") 11
Why is "[ansser)) " an answer aligned with human common sense?
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Target Template:
if abstractive_explare }}
Input Template:
Pick the option in 1ine with common to answer the question. Question: question }}
Options:
18 for k in rangecchoico Tength) $]
[t'. join ( aaaswer__eeae ckkiccsskk, choices [k]))
18 endfor
Target Template:
if answer _eawer_chhiee ces [choi ces. index (answer) ] }}
Answer Choices Template:
1III I III
Prompt for the original task intended by the dataset authors Input Template:
Question: {{ question }}
Options:
choices
join ("An= ") F}
Explain why a human: would choose "I (answer) to answer the question above:
Target Template:
{ abstract.vveeexlaaattioo }}
Prompt not for the original task intended by the dataset authors Input Template:
Question:
question
Options:
[( choices | join ("An= ") }}
The answer is "[[ answer } because
Target Template:
it abstractive explaration 11
109

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Input Template:
Pick the option in 1ine with common sense to answer the question. Questions: {f question }}
Options:
(f anawer_choicer
join ("An= ") }}
Target Template:
i6 anawer }}
Answer Choices Template:
{
choices
join (1111")
Prompt not for the original task intended by the dataset authors Input Template:
Here's s question: H question H
Here are possible answers to this question:
{{ choices I ("In ") }}
believe the correct choice is "((answer) ) here's why:
Target Template:
{ abstrratt.ve.eellaattiioon }}
Input Template:
[f question }}
(3 for in range (choices length) $)
[('. (
[k], choices [}]
18 endfor
The best answer is
Target Template:
answer choices answer_choices (choices. index (answer) ] }
Answer Choices Template:
A 11I B 111 C III
DIIDIN IIIE
1.7.2 COSMOS_QA
Dataset from Huang al. (2019). Used in training.
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Data Example
Key
Value
answert answerl
None of the above cheices This person likes music and likes to the show This person only likes Geod Old Mar and Person other Bands is not on tour and this person can not... Good 0ld War and person L saw both of these ba...
answer2
answer3
context
id
label
question
the future will this person go to see other b...
Prompts
Prompt for the original task intended by the dataset authors Input Template:
Based on the context and the anawer, generate a question.
Context: [[context))
Answer:
{8 1f label -- 0 $]
[answer0))
elif label
$]
(answerll)
(8 elif label == 2 8 [answer2]]
18 clif label == 3 $
(answer3)]
8 ondif 8)
Target Template:
[qqusstion]
Input Template:
Read the following context and choose the best option to answer the question.
Context: (( context }
Question: question }
Options:
if anawer_cho ces
join ("An - ") }
Target Template:
answer choices [label] 1
Answer Choices Template:
[nnseer01 11 III Hansweri III Hanswer2]) III [[answer3]
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Input Template:
Read the following context and answer the question.
Context: {f context }
Question: {f question }}
Answer:
Target Template:
enswer_choicer [label]
Answer Choices Template:
(answcr0)} III Hannsweri] III ((answer2)) III ((nnser3))
Input Template:
Read the following context and choose the best option to answer the question.
Context: context 1]
Question: {{ question. }}
Options:
A. {f answerd }}
B. {( answerl }]
C. answer2
D. {( answer3 }}
Target Template:
{ answer_choices [labol] }
Answer Choices Template:
I
Input Template:
11 context }}
According the above context, choose the best option on the fol following question.
Question:
|I question 1]
Options:
(ansser choices Chosice
join(")n ")
Target Template:
( (answer choices [abell] }}
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Answer Choices Template:
( (ansxer0]) III { [arswerl]] III ((answer2)) III ((answer3))
Input Template:
[f context H]
{f question }]
A. { answerd }}
B. {f answerl
C. { answer2 }
D. { anawer3 }
Target Template:
choices answer_choices| label]
answer_
Answer Choices Template:
A
11I B III III
Prompt for the original task intended by the dataset authors Input Template:
{f context 11
Question: {f questior }}
The answer to the above question:
Target Template:
answer_chop [1abel]
Answer Choices Template:
([nnserr]) II| [answerl]] III ((answer2)) III { ((anser33)
Input Template:
{ context 1] question 11 (( anawer_choicer join (")n ") 1)
Target Template:
[ anawer nhawer_choiee chor-rchhiiie ces [1abel] }
Answer Choices Template:
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[answero]] 1I1 Hansserlll III ([answer2)) III ((answer3)) )
Input Template:
if context }}
According to the above context, choose the best option to answer the following questior.
Question: questior }}
Options:
A. {f answerd I]
B. {f answerl }]
C. {f anawer2 }}
D. anawer3
Target Template:
if anawer_choices [label] }
Answer Choices Template:
A 111 B C
Input Template:
if context }]
if question }}
Pick the best
from the following options:
A. {f answero
B. {f answerl }]
C. {f answer2 I]
D. answer3
Target Template:
if answer_choices [label] }
Answer Choices Template:
11I B DII
Input Template:
if context })
According to the above context, answer the following question. i{ question }}
Target Template:
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(answer_choices [label]] }}
Answer Choices Template:
[answer0)] III [[nnswrrl]] III ([answer2)} III ((answer3))
Input Template:
if context }}
if question }1
Pick the best answer from the following options: answer seswer_choices join ("\n
Target Template:
answer choices mnswer_choices [label] }
Answer Choices Template:
(answer0)) III [fnnswerl III ((answer2)) III
( ((nsser)3) )
Prompt for the original task intended by the datasct authors Input Template:
[qqessiion]
Target Template:
answer_choicee [label] }
Answer Choices Template:
(annwerr0]] III [[nnswelll] III ([answer2)) III ((answer3)
1.7.3 DREAM
Dataset from Sun et (2019). Used in training.
Data Example
Prompts
Prompt for the original task intended by the dataset authors Input Template:
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Key answer choice
Value
Continue her dancing class.
['Consuit her dancing teacher.', ' 'Take a more inte...
dialogue
['M: I am considering dropping my dancing class. I...
dialogue id
5-510
id
0
question
What does the man suggest the do?
Read the below conversation.
(ddialogul [1-1] join ("\n\n")
what would the listerer say?
Target Template:
(-1])))
Prompt not for the original task intended by the dataset authors Input Template:
Given the question "I iquestion] " and the answer " (answer) )", write conversation that might have happened.
Target Template:
[dd a logue
join ("\n\n") FF
Prompt not for the original task intended by the dataset authors Input Template: {di a ogue [1:] join ("An\n")))
What was said before this conversation?
Target Template:
[ddialogee [0]1)
Input Template:
Dialogue:
!(dialogue join ("AnAn"))
Question: ((question)
[[answer_choiie [C]]]
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{(answer_cholcos [1] }
[ [2]
Target Template:
(answer)) }
Answer Choices Template:
i(choice
join (" (1)))
Input Template:
Read the following conversation and answer the question,
(dialogue join ("Nr\n""
Question: [Iquestion)
[(anawer_choices [0]
{(answer_choice) [1]
[[answer_choices| [2]
Target Template:
[answer] | }
Answer Choices Template:
cchoice
join (1)))")
1.7.4 OPENROOKQA MAIN
Dataset from Mihaylov et al. (2018). Used in evaluation.
Data Example
Key
Value
answerKey
choices
('label'; [ l'puppies learning new tricks', 'childre...
7-980
question_stem
The sun is responsible for
117

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Prompts
Input Template:
(question_stem)
Choose an answer from this 1ist: {f snawer_chol ces join ("An= "> }}
Target Template:
[[answer_choicsss[[ "B":1, "C" :2, "D":3) [amswerkeyl1]
Answer Choices Template:
text { (chof cex.text join (*111)))
Input Template:
[iquestion_se stem]
which is the correct answer? {{ answer_choices (" }}
Target Template:
[answer__coicess|] [1"A" "B":1, "c":2, :2, "D":3) [answerKey]]] }}}
Answer Choices Template:
[chooicos.toxt join (1111" {)}}
Input Template:
( (question_stest
18 for in range (choices("tees length) $)
[(' -> ". "B", "C", "D" [k], choices "text" [k] [1]]
18 endfor $1
the right answer i1"A, B, C or D")) ?
Target Template:
(answerKey])
Answer Choices Template:
A
||| 1II
C III
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Input Template:
{ (question_stem))
Choices: [{ anawer_choicee join ("An= ") }}
Target Template:
[[ansser_choices| (ansser choices | :0, "B":1, "C" :2, "D":3) [answerKey] 111
Answer Choices Template:
(choices. text joir (" [11"))
Input Template:
[qquestion_ss stem] {( answer choices
join ("An= ") 11
Target Template:
(answer_choices [I :""A";,,, :0, "B":1, "c":2, :2, "D":3) [answerKey]] 11
Answer Choices Template:
(choices.tex text join ("
Input Template:
['questionsstml] ({ answer_choices join ("An- }}
Which is the correct answer?
Target Template:
[answer_chol cas :( "B":1, "C" :2, "D":3) [answerkey]] 1}}
Answer Choices Template:
choices. text
join (*111")
Input Template:
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(question_stem)
Pick the right answer from the list: {{ answer_choices join ("An= ") }
Target Template:
[ :0, "B":1," "C":2, :2, "D":3) [answerKey] 1 }
Answer Choices Template:
(choices... join (*111")
1.7.5 QASC
Dataset from Khot al. (2020). Used in training.
Data Example
Key
Value
answerkey
choices
('label'; ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H']...
combinedfact
Beads of water can be formed by clouds.
fact!
beads of water are formed by water vapor condensin... Clouda are made of water vapor. What type of water formation is formed by clouds? DE700EEC11200111AA222NEEE What type of water formation is formed by clouds?
fact2
formatted question
question
Prompts
Prompt for the original task intended by the dataset authors Input Template:
If tell you that I/combinedfact [0] capitalize]]((
combinedfact [1:] [trim("." }., and ask you the question "[[ question [0] lower 1)41 question[1: })", is the correct answer "(1
choices. text (0) [0] [loweri (( teoices.text [0] [1:] (trimti.''' }]"?
Target Template:
if answerKey =- choices. label [0] $) Yes {$ else $ No ($ endif $}
Answer Choices Template:
Yes
|||
No
Input Template:
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if fact1 [0] capitalize HH( Iarri//iiii 1h, and
[[fact2[]] lower 1)11 fact2 [1:] ferin/trimm" Hh. Given these facts, (1
question[9] [0) looerr }) [1:] }) among the following options:
{(answer_choiie
[ }}
Target Template:
18 for choice in choices cabee & ($ if choice -- answerkey $){
susser_choices [loop. index 1] 118 endif 8) endfor $1
Answer Choices Template:
(ccoo join (" ("111")}}
Input Template:
Fact 1: {{ fact1 [0] capitalize HH fact [1:] trimmtrim// }).
Fact ([fact2 (0) capitalize HH[ fact [1:] Itriml trim(" )).
Given the two facts above, {{ question [0] 1 loxer 11 (qquetiionooooooooooo [1:] [trimm(( }}?
Target Template:
18 for choice choices label $ [8 choice answerKey $H
answer_choice [loop. index 1] HH$ endif 8)18 endfor $
Answer Choices Template:
{choo cee.texx
join ("111"))
Input Template:
You are presented with the question "(I question F)" and the following answez choices:
[hanewer_choices join ("\n
}
Now knowing that {f fact [0] lower HH fact [acti[1:]] [1:]]][im/rrrmmm :) }) and
{ (fact2[0) [0] lower }H fact2 [trim/trim(". Hh, choose the best answer.
Target Template:
18 for choice choices label $) (8 choice answerKey 811 answer_choices [loop. index 1] HHS endif 8){8 endfor $)
Answer Choices Template:
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[cholces.toxx
join (* (1))
Input Template:
You are presented with the quiz "[[ question })"
But you don't know the answer, so you turn to your teacher to ask for hints. He says that "If fact [0] lower }Hl [1:]][]]]]]rrrrrr }}" and "((fact2[0] [0] llwer HH{ fact2[1:] |trim/trin(".. . }}"
So, what's the best answer to the question?
Target Template:
16 for choice in choices label $} {$ if choice == answerKey $ answer_cholcos [loop. index 1] H) ondif $){$ endfor $)
Answer Choices Template:
[cchoices. text join (" (1111)))
Prompt not for the original task intended by the dataset authors Input Template:
IE (f combinedfact [C] [lower HHF combinedfact [1: (1 trit 1,, then {( question [0] llwwer }} ((question [1:] ( ))?
Answer choices:
[[answer_choicos join - ") }}
Target Template:
18 for choice in choices. Label $1 18 if choice == answerKey $1
answer_choices choices Loop. index 1| 1188 endif () endfor $1
Answer Choices Template:
[choiices.tex join (oi(("111)))
Prompt not for the original task intended by the dataset authors Input Template:
Do you think the right answer to the question "([ question [0] llower } question[1:] }]" is "If choices. text [1] [lweer) choices pooices.text[11 [1] [1:] [trim(".") 11", given that
{ [combinedtact [0] [Lower) Ef combinedfact [1:] [trim('. !1?
Target Template:
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answerKey choices. 1ab.llalellll. [0]
Yes { clse No (% ondif 8
Answer Choices Template:
Yes
No
Input Template:
Fact (f fact1 [0] capitalize HH fact [1:] (trim//ttttt/mm }).
Fact 2: ((fact2[0) [0] capitalize HH{ fact2[1:]] (ttiitttimiim/// )).
Givenn two facts above, answer the question "(( question })" with the following options:
((answer ((answer_choices join ") 11
Target Template:
for choice in choices label $1 if choice answerKey $H
answer_choices dooo.. index 11 endif () endtor $1
Answer Choices Template:
(choi ces, text
join ("111")}
1.7.6 QUAIL
Dataset from Rogers et al. (2020). Used in training.
Data Example
Key
Value
['not enough information', 'to visit family', 'par... That fall came and went back to Michigan and the...
context
context.d
#001
Correct.answer.e
domain
fiction
#001_0
metadata
('author's 'Joseph Devon', 'title': 'Black Eyed Su... Why was this character sent away after each school...
question
question id question type
Causality
Prompts
Input Template:
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if context }]
Question: (f question }}
Options:
18 for in range length) $)
( (oonn(aaswer_cchoccc [k], answers [k])) endfor 8}
The correct answer is
Target Template:
answer_choices [corrocttanneerr_il }
Answer Choices Template:
A
111 B
111 c 111
Input Template:
if context }}
Question: (f question }}
Options:
answer_choices join(" An ")
The correct answer is
Target Template:
i6 anawer_ inawer_choices
))
Answer Choices Template:
(answers
join (
Input Template:
Read the following context and choose the option to the question.
Context: If context }]
Question: |I question })
Options:
for in range length)
{'. "ojoin [answer_choices [k], answers [}]])
$ endfor $}
Target Template:
{{ answer_choi [ Scorrect_anawe____ }
Answer Choices Template:
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111 I
Input Template:
if context }]
14 question }}
Pick the correct answer from following options:
{f anawer anawer_choices join ("An- ") }1
Target Template:
{f answer_choices [corroct_answr__iid] )
Answer Choices Template:
(answers
("
Prompt for the original task intended by the datasct authors Input Template:
{f context 1)
Question: {f question }}
===
The answer to the above question is
Target Template:
answer answer_choices ccorrect_answer,a }
Answer Choices Template:
[answerss
join ("111"))
Prompt not for the original task intended by the dataset authors Input Template:
{ context }}
According if question }}
above context, answer the following question.
Target Template:
answer_choiese hot.beosssoorrrrtt__ooooeeee
Answer Choices Template:
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fanswers
join (" ["
Input Template:
if context }}
if question }]
Pick the correct answer from the following options:
18 for in range (answers length)
[f' Join (lanswer_choloos (k), answers [
endfor $
Target Template:
answer_choiee choices correct answer id]
Answer Choices Template:
A 111 B 111 c 1II
Input Template:
{ context }}
{ question }}
18 for in range (answers length) $)
{ join tanswer_choices [k], answers [k]]) }}
18 endfor $)
Target Template:
if answer nnawer_choicee ces [correct_answer__f] }
Answer Choices Template:
11I CIID
Input Template:
if context 11
According to the above context, choose the correct option to answer the following questior.
Question: questior
Options:
18 for in (answeareere length) $
(t' joooin [answer_cholcos [k), answers [k]]) }}
(8 endfor $}
Target Template:
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answer_choices [corroct_answe__idd]
Answer Choices Template:
Prompt not for the original task intended by the dataset authors Input Template:
Read the following context and answer the question.
Context: {f context }
Question: question }
Answer:
Target Template:
{{ http:rrccccooossooorreeeeeaeeeee }
Answer Choices Template:
(answerss
join (") (1)))"))
Input Template:
context }} question }} {{ answer_chopl choice_ join ("An= ") }}
Target Template:
answer choices correct answer id] 11
Answer Choices Template:
(answers
join (" ("111"))
Input Template:
!( context 1]
According the above context, choose the correct option to the following question.
Question: {f question }}
Options:
(f anawer_chee anawer_choices join ("An- ") }}
Target Template:
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answer_choices [corroct__nswer_idd
Answer Choices Template:
(answers
join (" (111)"))
Input Template:
Road the following context and choose the correct option to answer the question.
Context: {{ context }
Question: {{ question }}
Options:
{{ anawer_choi ces
join }
Target Template:
answer_choices [corroct_answer__aa }
Answer Choices Template:
(answers
join (" (1)))
1.7.7 QUAREL
Dataset from Tafjord et al. (2018). Used in training.
Data Example
Key
Value
id
QuaRel_Vi__r__ee
answer index
logical forms
l' (infer (speed higher worldl) (smoothress higher qrel (speed, higher, worldl) -> qrel (smoothness, hi...
logical form pretty wited_literals
l'worldl': ['ice'), 'world2'; ['snox' Mike snowboarding on the snow and hit piece
question
Prompts
Prompt not for the original task intended by the datasct authors Input Template:
Question: ((question)
Do not use [["A")) and [("B")] to answer the question but instead, choose between "I sunsser__choces 1" and "I "[[answe__choice) [1]))*,
Target Template:
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(answer_choices [answer__ndcx.
Answer Choices Template:
[world_11s Silliierals..orr [01}) III ([wor1d]iiterals worl d2 [0]))
Prompt for the original task intended by the datasct authors Input Template:
Here's logic test: [qqusstion]]
Choose the answer between [[aaswer_choicee [0] and "( [[[aaweer_choocsss]]]]
Target Template:
[annwer_choices minswer_inder, |
Answer Choices Template:
[[world_licerals.worlool [011} III ([world_literals worl d2 [0]))
Prompt not for the original task intended by the dataset authors Input Template:
Here's s short story: [qquestion)..
What is the most sensical answer between [(answer_choices [0]) and "( [[[anwer_choicess]]] (1]))"?
Target Template:
((answer_choices choices aasswrr index |
Answer Choices Template:
[Norlld_literrls.wwrr [011) III [[world_literals..r wor 1d2 [0]
Prompt for the original task intended by the dataset authors Input Template:
Choose between "I [[aaswwrrcchhicee [0]))* ) and "((anawer_chol ces "
Question: [qquestion)]
Target Template:
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[answer_choices [an:wer__ndcx]
Answer Choices Template:
[Noorld]ltterrls.worddd III ((world_literalss_ world2 [0]))
Prompt not for the original task intended by the dataset authors Input Template:
am teating ing my students' logic. what is the answer they should choose between "( ((answer_che ces [0]]]* )
and (answer_choo ces [1]))"?
Logic test: [[question]]
Target Template:
(answer_choices (answor__iddc]]
}}
Answer Choices Template:
(world_literals. worldl [011} III ([world_literals..e) worl [0]))
1.7.8 QUARTZ
Dataset from Tafjord et al. ("2019"), Used in training,
Data Example
Key
Value
answerKey
A
choices
('label': ['A', 'B'], 'text'; ('scarce', 'plentifu... QRQA-10385-4
para
Many of the worlds people live with water scarcity.... ('effect_prop' 'population growth', 'cause_dir_st... QRSent-10385
para_anno
para_id
question
John's town used have lots water, back wher d'moreeeffec__dr>" 'several thousand', "less_effe..
question_amm anno
Prompts
Input Template:
Use information from the paragraph to answer the question.
Question:
18 it
in question $)
question trim(".3!")
replace (" ("_,,, mnswer_choices join ("
") FFEE "?" 11
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clse $ { question trim(".3!") FF (f answer_cholces join(" or ") HHE "?"
18 endif $}
Paragraph
16 para |]
Target Template:
(annser__hoices [choices label. index (answerKey) }}}
Answer Choices Template:
ccooices.tex join ("111")
Input Template:
[6 paza 11
18 if
in question
if question
trim("..!") replace (" ", answer_choices join or ")) HH{( "?" H)
18 clsc $1
"1 question
endi trim(". (( answer_choices join (" or ") }) "?" }
Target Template:
(answer f choices label. index (answerKey) 111
Answer Choices Template:
[choicessten joir. ("111")) ")}
Input Template:
Use information from paragraph to answer the question.
Paragraph
16 para }}
Question:
16 if
in question $)
if question
trim(". _2"") replace ", answer_choices join })
")) Hl{( "?"
18 else &)
[f question trim("..11 F1 [I answer_choicn | jeint" ") 114 "?"
11
18 endii
131

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Target Template:
(aanwer_choiie) [choi ces label. index (answerkey) 1}}
Answer Choices Template:
(choices... toxt
join ("111")
Input Template:
Answer the question based on the following text.
Question:
:8 1f ' in question $
if question trim("..!")
zeplace answer_choices
join (" or
")) HH{ "?"
13 else $1
if question trim(".3!") FF (( answer_choices join(" (" or ") HH{ "?"
11
16 endi1
rext:
14 para }}
Target Template:
faanwee__hhoiee cchoices.... label. index (answerKey) }|
Answer Choices Template:
['choices athoices.ten join
Input Template:
Answer the question below:
16 1f
in question $}
if question trim(".?!")
replace (" answer_choices
join (" or
"))
FH{ "?" }
:8 else $}
11 question trim(".311) 2!") 1) (( answer_choices join( or ") 111 "?"
11
18 endit 81
Assuming that:
16 para }}
Target Template:
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(answor_choices (choices label index (answerKey) 11}
Answer Choices Template:
[choiccs.tee
join (" (*111))
Input Template:
Read the passage below and choose the right answer the following question (choices are {f answer_choices join or ") H ):
[ paza 11
question $}
if question trim("..!") replace (" ("_,,
_", answer_choices join or
")) HH{ "?" }
{8 else $1
16 question trim(".3!") inswer_choices join(" }
ondif 8)
Target Template:
(answer_choices [choices. label. index (answerkey) }}}
Answer Choices Template:
!(choices.ten text join (" ||] {}}}
Input Template:
{6 para }}
Having read the above passage, choose the right answer to the following
question (choices are {f answer_choices | join(" or ") H] ):
:8 if
in question $} (rim!".2!")) replace
If question
answer_choices join(" or
")]
11f1 "?" H1
18 else $1
[1 question trim(".319 H1 {I answer choices | join(" ") 114 "?"
endif
Target Template:
[annwer__hoo ces [choi ces. 1 label. index (answerKey) 1}}
Answer Choices Template:
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[cholces.toxx
join (* [1))
Input Template:
Given the fact that:
if paza }}
Answer the question:
[6 if
in question 8 if question trim(".3!") replace answer_choicee join (" or
")) HH{ "?" })
[8 else $)
[1 question trim(".319 (3!") YF {f enswer_choicee join(" or "} 114 "?"
{8 endif 8)
Target Template:
[aanswer_chhieee choices (choices label. index (answerKey) 111
Answer Choices Template:
(cholces.. text text
join (*11 {)}}}
1.7.9
RACE HIGH
Dataset from Lai al. (2017). Used in evaluation.
Data Example
Key
Value
answer article
Studies show that you may be lied to every day any...
example_ik id options
high10001.tas txt
('harmful', 'easy', 'interesting', 'common'1
question
From Para.1 learn that lying is very
Prompts
Prompt for the original task intended by the dataset authors Input Template:
18 candidate ["A", "B", "c", "D"] | choice $)
Article: ((article)) Question: [[question)]
Yes or no, is the answer "[[
options. iilonss0,0ei optionss options options. (("A"0 "B":1, "C":2, "D":3) [answer] ??
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Target Template:
18 if candidate $
Yes
{8 else 8)
No
16 endlf $}
Answer Choices Template:
Yes
|||
No
Prompt not for the original task intended by the dataset authors Input Template:
write multi-choice ce question for the following article: Article: ((article))
Target Template:
Question:
[[question]]
Options:
[("A")] [[options.00 01]
[("B)]] [loptions.1]]
["c"]] ((options.2))
[("D")} ((options.3))
Answer:
(answer))
Input Template:
I'm taking test and have to guess the right answer to the question
after the article.
Article: ([article]]
Question: [[question)]
Options: (["A")]: [/options.0]]
[("B"Hi: [ooptions.1]]
[("C")]: [ooptions.2]
[("D"]]: [[options.3] 3]]
Target Template:
[answer]
Answer Choices Template:
A 111 B 1 1 C
Input Template:
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Read the article and select the best answer. Article: [(article)]
Question: [[question]]
Options: [{"A")]: [joptions.0]]
["""]]: [[options.]]]
("c"]]: (optiions2]]
((DD))): Hoppiooss.3] 3]]
Target Template:
[answer]] | |
Answer Choices Template:
1II s C
Prompt not for the original task intended by the dataset authors Input Template:
Write multi-choiee question for the following article, with the given choices and answer:
Article: [[rrticle))
Options:
[("A"H] [(options.0] 0))
[("B))) [(options.1))
["c"]] [(options.2)]
[("D")) ((options.3))
Answer:
[[answer)] {{
loptions... options. options op:ions. 33 [("A":0 "B":1, "C";2, "D":3) [answer] 11
Question:
Target Template:
('question) 1
Input Template:
Read the following article and select the best answer.
Article: ([article))
Question: [qquestion]
[(answer_chol) choices join ")
Target Template:
(answer wwwpcwerrchhieesseeeeeeeee "inees""""""""" :","; "B":1, "C";2, "D":3) [answer] ]
Answer Choices Template:
136

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{f
options
join (*11)")
Input Template:
(article]]
[question]]
["A)"]] ( [[options.C]] .0]
((B) "}} { [optioos.1]]
("c) "}] [[options.]]
("D)" "|I [oopionss.3]
Target Template:
[answer)) }
Answer Choices Template:
11II III D
Input Template:
Read the following article and answer the question.
Article: [[article]]
Question: [(question)
Answer:
Target Template:
i answer_choices =1"A"0,, "B":1, r2, "D":3) [answer]] }}
Answer Choices Template:
options
join (111)))
1.7.10 RACE MIDDLE
Dataset from Lai et al. (2017), Used in evaluation.
Data Example
Prompts
Input Template:
137

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Key
Value
answer
C
article
Take class Dulangkou School, and you'll see 1... middlel.txt
example_id
options
[ ('take care of the whole group', 'make sure that e...
question
A discipline leader is supposed to
Read the article and select the best answer.
Article: [(article))
Question: [[question]]
Options: [["A"]]: [/options.01 0
[(B")]: [[options.1]]
("c")]: ([options.2)
("D"):: [[options.3]
Target Template:
(answer)) }
Answer Choices Template:
11I B I
Input Template:
Read the following article and answer the question Article: [article]]
Question: [[question)]
Answer:
Target Template:
[ answer answer_choi ces (I =1*A":0, "B":1, :2, "D":3) [answer]] }}
Answer Choices Template:
{
options
join ("111") }
Prompt for the original task intended by the dataset authors Input Template:
18 set candidate ["A", "3", "c", "D"] choice $
Article: ((article)) Question: [[question]]
Yes or no, is the answer "[{
options. 00, options.,, options 2, opt (("A":0, "B":1, "C":2, "D":3) [answer] n?
Target Template:
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:8 candidate answer & Yes
: clse
$}
No
endi
Answer Choices Template:
Yes
|||
No
Input Template:
(article]]
[question]]
("A) { [[options.]]]
((B) "}} [[options.]]
((CC)") "1I Hoppionns.2]
((")) "H [oppioons.]] 33)
Target Template:
[answer]]
Answer Choices Template:
A 111 B 111
III
Input Template:
Read the following article and select the best answer. Article: ((ariccle)
Question: [Iquestion)
(fanswer_choices (")nn")n- ")
Target Template:
[(anser__choicss : ",":1, "C":2, :2, "D":3) [answer]
Answer Choices Template:
{{ options
join (
Prompt not for the original task intended by the dataset authors Input Template:
Mrite multi-choiee question for the following article, with the given choices and answer:
Article: [article])
139

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Options:
[("A")] [[options.)) 0)]
[("B"]] [[options.l]]
[("c")) ([options.2)
[("D")] ((options.3))
Answer:
[(nsserr) II
options. 0, options .1, options 2, options. "==":,, "B":1, "C":2, "D":3) lnnserr
|
Question:
Taget Template:
[qquestion]
Prompt not for the original task intended by the datasct authors Input Template:
Write multi-choice question for the following article: Article: ((article))
Target Template:
Question:
:(question]}
Options:
["A"]] [(options.C)
["B"]] [(options.1] 11]
[("C)) [loptions.2]]
(("D")) [[options.3)
Answer:
[answer)]
Input Template:
I'm taking test and have to guess the right answer to the quest sin
after the article.
Article: ((article)
Question: [[question]]
Options: [["A"]]: [loptions.01
[("B"]]: [[options.]]]
[("C"H: [oppiioss.2]
[("D")]: [loptions.3] .3]
Target Template:
((nnwer)) }
Answer Choices Template:
A ||| 3 111 C III
140

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1.7.11 SCIQ
Dataset from Johannes Welbl (2017). Used in training.
Data Example
Key
Value
question
What type of organism is commonly used in preparat... viruses
distractor3
distractor1
protozoa
distractor2
gymnosperms
correct_answer
mesophilic organisms
support
Mesophiles grow best moderate temperature, typi...
Prompts
Input Template:
Q: ((question))
A:
Target Template:
(answer__hoicce) )3))}
Answer Choices Template:
[distractorl]] 1I1 [(distractor2))
III [ddistracoor]]
III
{ correct answer)] correctt_nsserr])
Prompt for the original task intended by the dataset authors Input Template:
18 set order [10, 1, 2, 31.
[0, 1, 3, 21, 1C, 2,
31,
10, 2, 3, 11.
10, 3, 1, 21, 10, 3, 2, 11.
11,
0, 2, 31,
11,
0,
3, 21
11, 2, 0, 31,
[1. , 2, 3,
0],
[1,
3,
0,
21.
[1, 3, 2, 0].
[2, 1, 0,
31,
[2,
21,
[2, 0, 1, 31,
[2, 0, 3,
11.
[2,
3,
1,
0].
[2, 3 0, 1],
[3, 1, 2,
01,
13,
1,
0,
21,
13, 2, 1, 01,
[3, 2, 0, 11,
[3,
0,
1,
21,
13, 0, 2, 11]
choice
0: ((question)
Choices:
{( anawer_chol ces [order [0]] }}
( answer choicesshhhceeseeeeeeeeee [order [1] 1
answer answer_choices order ( 11
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answer_choicen [order [3]] F1
A:
Target Template:
(answcr_choices [3])]
Answer Choices Template:
[ddistractor]] III ((distractor2]) III [[distractor3] III { [correct_answer])
Input Template:
: set order [[O, 1, 2, 31, [0, 1, 3, 21, [o, 2,
31,
[0, 2, 3, 11,
[o, 3, 1, 21, [0, 3, 2, 11.
[1, 0, 2,
31,
[1,
0,
3, 21,
[1, 2, 0, 31,
[1, 2, 3, 01,
[1,
3,
0,
21,
[1, 3, 2, 0
[2, 1, 0, 31,
[2,
1,
0,
21,
[2, 0, 1, 3
[2, 0,
11,
[2,
[2, 3, 0, 1],
[3, 1, 2,
0],
13,
1,
0,
21.
[3, 2, 1, 0].
[3, 2, 0, 11,
[3,
0,
1,
21,
[3, 0, 2, 111
choice $
Q: ((question))
Read this paragraph and choose the correct opt option from the provided answers:
(supportt]
Choices:
answer_choices (order [0]]
{{ anawer_choicee [order [1]] }}
{ answer_choices [2]
(( answer_choiced (order 3] F]
A:
Target Template:
[[ansver_choi ces {3])}
Answer Choices Template:
[ddistrctoor] 1I1 [ddisractoo22]
[ddistrcctr3]]
[correct answer]] (correct_enswer])
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Input Template:
:8 set order [[O, 1, 2, 31, [0, 1, 3, 21, [C, 2, 1, 31,
[0, 2, 3, 11,
[0, 3, 1, 21, 10, 3, 2, 11,
[1, 0, 31,
[1,
21
[1, 2, 0, 31, ,
[1, 2, 3,
0],
[1,
3,
0,
21
[1, 3, 2, 0]
[2, 1, 0,
31,
[2,
1,
0,
21,
12, 0, 1, 3],
[2, 0, 3, 11,
[2,
3,
1,
01,
[2, 3, 0, 11,
[3, 1,
01,
[3,
0,
21,
13, 2, 1, 01,
[3, 2, 0,
11,
[3,
1,
21,
13, 0, 2, 111
choice 8)
Answer the following question given this paragraph:
(support|]
Q: ((question))
Choices:
{{ anawer_char answer_choices [order FF
( enswer_choices(ordes
( answer_choices (order [2) [
{ anawer_choi ces [order [3]] }}
A:
Target Template:
(aanseer_hhiieee {3])}
Answer Choices Template:
[distractorrl] 1I1 I [ddistractor21 III [disstactor33] III [correct_answer])
Input Template:
Answer the following question given this paragraph:
(support
Q: ((question))
A:
Target Template:
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{answer_choicos [
Answer Choices Template:
[distractor1]] 111 [(distractor2]] [correct_answerr
III
(edistrccoo33) III
1.7.12 SOCIAL.LQA
Data Example
Key
Value
answerA
like attending
answerB
1 like staying home good friend to have Cameron decided to have barbecue and gathered he...
answerC
context
label
1
question
How would Others feel as result?
Prompts
Input Template:
heard that [[context]}
And was wondering [question]) }
Target Template:
[[answer_choicss [1abe1 int 1]})
Answer Choices Template:
(answerA]] III [answerBB] III [[aswerC))
Input Template:
context }
Given the context: [[questio]]]
Possible answers:
{ (answer Aanswer_choices
choices
join (", ")
Target Template:
144

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(answer_choicos [label
int
111)
Answer Choices Template:
[answerA]] III [(answerB)] III ((answerC))
Input Template:
18 set randon_answer__ - range (0, 2) choi [ set answers [answerA, answerB, answerc] $ [context]]
Given the question "[[question] ,", is "I [answers (raneerssrandnn (randon_deea answan [randon_answer__ [ valid answer?
Target Template:
{ if (label I int) randon_answer_ic Yes
[8 else $}
No
18 endif 3}
Answer Choices Template:
Ves
|||
No
Prompt not for the original task intended by dataset authors Input Template:
[context| F
Given that the answer to a question "H11"1": answerd, "2": answerB, "3": anawerC) [label]]] )", what is the question?
Target Template:
([question]
Input Template:
[context]] 1
viven context:
!qquestio) ion]]
Target Template:
145

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(ansxer_choicos [label
int
111)
Answer Choices Template:
[answerr]] III [answerB]]
[answerC))
Input Template:
Context:
[cooteet)
Question: ((question)]
which one of these answers best answers the question according to the context?
A: ((anawerA) }
B: ((answorB)
C: ((naaerrC) }
Target Template:
EEEMI": "A", "2": "B", "3"; "C" [label]]
[]] }
Answer Choices Template:
A |1| 111 C
1.7.13
SUPER_GLUE BOOLQ
Dataset from Clark al. (2019). Used in evaluation.
Data Example
Key
Value
idx label
passage
Persian language -- Persian (/prn, -n/), al... da iran and afghani stan speak the same language
question
Prompts
Input Template:
Passage: ((passage)
After reading this passage, I have question: ([question] 1? True False?
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Target Template:
label -1 8) (answer_choices [label]} endif $)
Answer Choices Template:
Palse
|||
True
Prompt from Brown et al. (2020)
Input Template:
14 passage }}
Question: question
Answer:
Target Template:
if label 1- -1 $
{ answer_choices [label] }
endif
Answer Choices Template:
No
Yes
Input Template:
11 passage }}
Having read that, wonder [[ question ]]?
Target Template:
19 1f label 1- -1 $
{ answer_choice [label] }
endif
Answer Choices Template:
No
|||
Yes
Input Template:
: ([passage])
Answer the following yes/no question: [question! Yes or no?
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Target Template:
18 if label !- -1 8) [[anser_choicse) [1abel]]} endif
Answer Choices Template:
No
|||
Yes
Input Template:
16 passage 11
Having read that, could you tell me {f question }1?
Target Template:
{8 if label != -1 8) mnswer_choices [label] }}
(8 endif $)
Answer Choices Template:
No |||
Yes
Input Template:
EXAM
:. Answer by or no.
Document:
((passage))
Question: ((question] in
Target Template:
18 if label 1= -1 {
(answer__hoolee choices [label]] }}
endif
Answer Choices Template:
No
|||
Yes
Prompt from Schick and Schütze (2021)
Input Template:
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Based on the following passage, {{ question })?
passage
Target Template:
if label !- -1 cchoices
{ answer cnswer_choices [label] } endif $1
Answer Choices Template:
No
|I|
Yes
Input Template:
Excrcise: read the text and answer the question by True or False.
"ext: ((passage)
Question: ((quastion))2
Target Template:
18 it label 1= -1 {)
(answer_choices [labell]} endif
Answer Choices Template:
False
I|| True
Prompt from Schick and Schütze (2021) Input Template:
i1 passage }} Based on the previous passage, (( question }}?
Target Template:
18 it label != -1 $111 answer mmsser_choices [abel] 1) endif $}
Answer Choices Template:
No ||| Yes
Input Template:
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{ passage
Q: [qquestion] True or Falsc?
Target Template:
18 Label != -1 8) (answer choices Labell 1} endif $}
Answer Choices Template:
False
||| True
1.7.14
SUPER_GLUE MULTIRC
Dataset from Khashabi er (2018). Used in evaluation.
Data Example
Key
Value
answer idx label
Children, Gerd, or Derian Popa ( ('paragraph': 0, 'question': 0, 'answer': 0}
paragraph
While this process moved along, diplomacy continue... what did the high-level effort to persuade Pakista...
question
Prompts
Input Template:
(paragraph])
Question: ((question) found this answer "[(answer)) }.. Is that correct? Yes no?
Target Template:
[8 it label != -1 $) (answer | label] ( endif $1
Answer Choices Template:
No
|||
Yes
Prompt from Schick and Schüitze (2021)
Input Template:
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if pazagraph }] Based on the previous passage, (f question } Is "{{ answer }}" correct answer?
Target Template:
{ if label
!- -1 8)1 answer_choices [label] () endi $
Answer Choices Template:
No
||| Yes
Input Template:
(paragraph)]
Question: (qquestion)
grading my students' exercises. the "[ (annserr) " correct?
Target Template:
[6 if label != -1 $) (aaawar_choiiae [label] {} endif $}
Answer Choices Template:
Input Template:
paragraph if question 11
Would it be good to answer "(( answer ))"?
Target Template:
[8 1f labol 1= -1 $)1 answer_cholcos [label] () endis $)
Answer Choices Template:
No
||| Yes
Prompt from Schick and Schütze (2021) Input Template:
16 paragraph 11
Question: questior Is answer 11?
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Target Template:
if label != -1 $)11 answer_choices [label] () endi $
Answer Choices Template:
No
||| Yes
Input Template:
(ppragraph))
Decide whether "[ "[[answer]]" is a valid answer to the following question: [question]] Answer yes or no.
Target Template:
(8 if label != -1 8) (answer, choiicesscceeeeeeeeeeeeeeeeeeees label] [1]]]])) (8 endif $
Answer Choices Template:
||| Yes
Prompt from Schick and Schütze (2021)
Input Template:
11 paragraph }}
Question: (I question }) Is the correct answer (f answer !!?
Target Template:
if label 1= -1 $)1 answer_choicos [label] () endis $)
Answer Choices Template:
No ||| Yes
Input Template:
Is "I (answor) ) a correct answer to the following question? Question: [qquestion]]
Rely on the following text: ((paragraph)
Target Template:
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label -1 $)1 f [aaswer_choices [label]
(labell) ()(8 endif $}
Answer Choices Template:
No
|||
Yes
Input Template:
(araggraph)
Question: [(question)
think "( "((answer) valid answer. Could you confirm?
Target Template:
(8 if label != -1 8) (answer chtiiesshccceeeeeeeeeee [abel])] endif 8
Answer Choices Template:
No ||| Yes
Input Template:
paragraph { question }}
was going to say "I answer })", Does that sound right?
Target Template:
if label -1 8)11 amswer_choicen [label] () endis
Answer Choices Template:
No |||
Yes
1.7.15
WIKIJIOP ORIGINAL
Dataset from Welbl et al. (2018). Used in training.
Data Example
Prompts
Input Template:
153

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Key
Value
annotations
[]
answer
1996 summer olympics
candidates
['1996 summer olympics', 'olympic games', 'sport') RH_train_
question
participant_of juan rossell
supports
[Te 2004 Summer Olympic Games, officially known
Information:
:$ for support in supports $ (f support }}
:8 endfor
:8 set question_split question.split ") 8) What object entity has the relation of 'H4 question_split:()) replace ", " "}]]" with the subject 'II question_split:1; join ")!''?
Choi ces: (fanswer_choices join ("An ") }
Target Template:
(answer)]
Answer Choices Template:
(candidates dates
join ("11"")
Prompt not for the original task intended by the dataset authors
Input Template:
Information:
16 for support in supports $1
{{ support
18 endfor $)
; set queation_split question.splice ') $}
What is the relationship between question question_split split join ")""""
and [[[answer]]"
Target Template:
if question_split(0) replace ("_", ") }}
Prompt not for the original task intended by the datasct authors
Input Template:
In foormation:
it for support in supports $1
support
18 endfor $1
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18 act question_split sucstion.eplit: ") 8)
What entity does 'if prcctioo_split[l1 | join(" ")E has the relation (( question_split replace (" "_", ") }} with?
Target Template:
(answer)) }
Prompt not for the original task intended by the dataset authors Input Template:
Information:
18 for support in supports $1
(f support 11
{8 endfor $)
14 set qucstion_split question.split ") $)
Given the paragraphs above, decide what entity has the relation 'H question_split(0) replace("_", }} with "[[answer))'.
Target Template:
{ questiio__spitt11 join (" "}}}
Input Template:
Information:
:6 for support in supports $
{f support }}
[8 endfor $)
18 set question_splin
question.split (p ') $)
Given the information above, choose from the list below the object entity that exhibits the relation '(f question_spliteeee join(" "}]'.
replace("_",
")}}" with the subject "(( question_split
Choices:
[(anser__choiie join (")n }}
Target Template:
[answer))
Answer Choices Template:
(candidates
join ("
11
Input Template:
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Information:
16 for support in supports 8)
(f support }
{8 endfor 8}
13 set question_split question.splite ') 8]
After zeading the paragraphs above, we are interested in knowing the entity with which 'II question_split/1: relationship of 'I4 question question_spli:101 spli=10] answer from the choices below.
join(" "IH exhibits the
replace (" "" Find the
Choices:
[lanswer_choices join (*An ") }}
Target Template:
(answer)) }
Answer Choices Template:
(candidates
join (" (11"))
Prompt not for the original task intended by the dataset authors Input Template:
Information:
:6 for support in supports $
{f support 1
16 endfor $1
{ set question_split question.splic ') $)
Given the information, choose the subject and object entities that have the relation 'I drestion_split// replace ("_" ") 11"
Target Template:
i questiion_slitt[11 join (" ") }} [ansser)
Input Template:
Information:
i8 for support in supports $1
(( support }}
16 endfor $)
(6 set question_split suestion.split ') $)
After reading the paragraphs above, choose the best answer for the entity that related to '(( relationship of 'H4 question_split/p00 replace "}]'"
join(" "}])' with the
Choices:
( {[answer_choices
join(")a ") 11
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Target Template:
(answer)) }
Answer Choices Template:
(candidates join ("
Input Template:
Information:
{3 for support in supports }}
(( support }]
16 endfor
{$ set question_splis question.split( ") &]
'[l qpestion_split[11 join(" "}]}" is related to which object entity
through the relation of "{ question_split replace ("_", "}}}'?
Choices:
[(answer_choices
join (")n ("in ") 11
Target Template:
[ansserr] |
Answer Choices Template:
[candidates
join (" "1
1.7.16 WIQA
Dataset from Tandon et al. (2019). Used training.
Data Example
Key
Value
label
lasee..label.s.chll
A
choices
['1abel': ['A', '8', (c'), 'text!: m'more', "less"..
metadata graph id metadata para id metadata path len metadata .uw.iiaaaauustiooiiii. metadata, ciaiitttuuuustiotttpretee question_para -prassstppppppppppppppp question_stem
144
1217
2
influence influence_graph:: 1tt.a_gp::::111:14111000000
INPARA EFFECT
['A tree produces seeds', 'The seeds are dispersed... supposs there will be fewer now trees happens, how...
157

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Prompts
Prompt for the original task intended by the dataset authors Input Template:
{ qucstion__ara_stopll.
join ("An- ") })
What might be the first step the process?
Target Template:
{ question_para_step first }}
Prompt not for the original task intended by the datasct authors Input Template:
:8 set process_
else Lissss_iiss 1ist - quest i on patiioo___tttpt-=-- if
$
process_list [:=1]]11]] join ("An- ") }}
What might be the last step of the process?
Target Template:
{
proceas_liss
1ast
}}
Prompt not for the original task intended by the datasct authors
Input Template:
What is the missing first step of the following process:
{f
join ("An= ") }}
Target Template:
{ question__ara_step
first
Prompt not for the original task intended by the datasct authors Input Template:
:8 set process_list list
if question_para_step(-1
=" else cerstioo_ppra_ttt__ $}
What is the final step of the following process:
(f process_list [:-1] | join (" ") }
Target Template:
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{f process_list last
}}
Input Template:
Process:
If puestton_prra_step | Doin("An- ")]]
Question: (questioo_stem))
How docs the supposed perturbation influence the second effect meneioned. Answer by ("more, less or no effect")]
Target Template:
(ansser Label replace ("_", " ") ||
Prompt not for the original task intended by the datasct authors Input Template:
Process:
(( qucstion__pra_stopp
| (" ") }}
(question_stem)
Which the following is the supposed perturbation?
(("directly impacting a step of the process"]| ("indirectly impacting step the process"
[["not impacting any step of the process")]
Target Template:
"indirectly impacting step of the process", DOUTOFPARA_DISTRACCO "not impacting any step of the process", "INPARA_RAN SEPPARARRFFRCT.: "di rectly impacting step of the process")
[( [""EXOGENOUS_EFPECTT"
Input Template:
Process:
(f Leeation_parasstee | Join("An- ")]
Question:
[qquestion_stem)
[("A: more"]] [("B: leas")]
(("C: no effect"))
Target Template:
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[ansxer__aabll_s__hhhol...
Prompt for the original task intended by the dataset authors Input Template:
Process:
{{ question_para_ster | join ("An= ") })
Perturbation hypothesis: (iquestionn stem]]
Does the supposed perturbation have an effect (direct or indirect) the process?
Target Template:
EEE"EXOGENOUS SEETENNOOSSEEFCT: EFFECT": "yes", "OUTOFPARA DISAPPAARRDDSTTRTTRRRR... "no", :FFECT; "yes") autaaaaataauussiioiroi
1.7.17 PIQA
Dataset from Bisk et al. (2020). Used in evaluation.
Data Example
Key
Value
When boiling butter, it's ready, you goal
label
soll
Pour it onto a plate
sol2
Pour it into a jar
Prompts
Input Template:
Goal: ([goal)]
Which is the correct ending? [[sol1}]
((so12)
Answem:
Target Template:
{
(answer choices cansier_choices [abell }
Answer Choices Template:
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{(sol11) III {
Input Template:
[(SSolution 1"11: [[so11)] ("Sollution 2"]]: [1so12]]
Goal: ((goal))
Given the goal, what is the correct solution?
Answer by copying the correct solution
Target Template:
(answer
([answer_choices choices [abel] }
Answer Choices Template:
[(so11)1 III
[[so12))
Input Template:
Sentence: [[goa]]]
Choice [[answer_choices [0] F]: [[so1]]]
Choice ((answer_choices [111): ((so12))
what is the index of the correct choice for ending for the sentence?
Answer:
Target Template:
( finswer_choicee [label] }
Answer Choices Template:
111 2
Prompt for the original task intended by the dataset authors Input Template:
Givee goal and a wrong solution, rewrite it to give a correct solution.
Goal: [[goal]] Solution: [soll, sol2] [1 labell
Corrected solution:
161

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Target Template:
[sol1, so12] [1abel]]}
Input Template:
Finish the following sentence with the best choice: ((goal))
Choices:
[[sol1]]
[[so12]]
Answer:
Target Template:
(answerr_hoo choierr_chhhiee ces [label]] }}
Answer Choices Template:
[(sol1}) III [{so12))
Prompt for the original task intended by dataset authors Input Template:
[goal]]) ([so12)) Does this phrase make sense?
Target Template:
(anawer_choices [label]]] }}
Answer Choices Template:
No
|||
Yes
Input Template:
Given a goal and 2 solutions, choose the most appropriate solution. Goal: [[goal))
[["Solution 1")): [[so11]] [("Solution 2")): [[8012]]
Answer by returning either (["Solution 1"]) or (I"Solution 2"
Target Template:
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(answor_choices [label]] }}
Answer Choices Template:
Solution
I1I Solution 2
Prompt not for the original task intended by the dataset authors Input Template:
it if it doesn't make sense. IE it makes sense, just return it as the answer. Input: ([goal)) 11so12 [0] .lower () so12[1:]1]
Given sentence,
Output:
Target Template:
ifgoal] H} [[aa11 [0]. lower () + sol [1:), so12[0] lower ()
so12[1:]] [1abel]]]
Prompt not for the original task intended by the datasct authors Input Template:
[ggoal]] }}
Target Template:
{ [so11[0] llwer () soll[l:), so12 [0] lower () so12 :]] [label])) )
Prompt for the original task intended by dataset authors Input Template:
Docs this phrase make sense?
[ggoal)) [[soll [0]. lower + col1[1:1))
Answer with
or (fanseer_hhoices (1]))
Target Template:
( (lanswer_chhiiee choices [abell] }}
Answer Choices Template:
Yes
|||
No
Prompt for the original task intended by the dataset authors Input Template:
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Sentence: [[goal)) 1isoll [0] lower soll[1:] If the sentence does not make sense, correct it so that it does make sense. Otherwise, just copy it.
Answer:
Target Template:
{goalll [[[o1] [0].loer so12 [1:1] [label]] }}
lower soll[1:), so12[0] lower
1.8 SENTIMENT
1.8.1
MAAZONNPOLARITY
Dataset from McAuley and Leskovec (2013). Used in training.
Data Example
Key
Value
content
This sound track beaut ! It paints the sene...
label
Stuning ng even for the non-gamer
Prompts
Input Template:
Fitle: [[title]]
Review: ([content))
Is the review positive or negative?
Target Template:
[(answer_choi ces [bbel]}
Answer Choices Template:
Negative ||| Positive
Input Template:
Based on this review, would the user recommend this product?
===
Review:
([content)
Answer:
Target Template:
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(answer_choices [label]} }}
Answer Choices Template:
No
Yes
Input Template:
this product review positive?
Vitle: ((title))
Review: [fcontent]]
Answer:
Target Template:
[[answer_choices| [label]] }}
Answer Choices Template:
No
|||
Yes
Input Template:
Citle: [[title]]
Review: [[content)]
Is this product review negative?
Target Template:
(aaswer_choices [label] }}
Answer Choices Template:
Yes
|||
No
Input Template:
Fitle: ((title)
Review: [[content)]
Does this product review convey a negative or positive sent sentiment?
Target Template:
[answer_choices [label]] }}
Answer Choices Template:
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Negative |I|
Positive
Input Template:
I8 there a negative or positive tone to this product review?
Citle: ([title)]
Review: ([content)]
Answer:
Target Template:
(aaswerr choices [abel] }}
Answer Choices Template:
Negative ||| Positive
Input Template:
Here is review left by customer product. Would you say he
(answer
(aanwer_choices choices [1]]] ((answer_choiie choices [0]]12
Citle: [[title]]
Review: ([content)] )
Target Template:
(answer_choices [label]]}
Answer Choices Template:
dissaaisfied |I| satisfied
Input Template:
You are considaring whether to buy product. You look at the reviews.
Mould the following review [fanswer_ [aanser_ehhiieee choices [0]) or ((answer_choices choices [1]]] the chances of you buying the product?
Review title: [[title]]
Product review: [[content]]
Target Template:
(answer_choicas [label]} }}
Answer Choices Template:
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decrease III
increase
Input Template:
Citle: [[title)]
Product review: [[content]]
Would you say this review ew depicts the product in (tanswer_chote ces [1]}}
or Sranawer_choicee ces [C]]} light?
Target Template:
[annwerr_cho [1abel]}}
Answer Choices Template:
un unflattering III flattering
1.8.2 APP-REVIEWS
Dataset from ?. Used in training.
Data Example
Key
Value
date
October 12 2016
package_name
com. mtt.mannt_it.teee ieaatz_it.rfanalyerr Great app! The new version works my Bravia
rfanalyzer
review
Prompts
Prompt for the original task intended by the dataset authors Input Template:
Given this review: "I "[veveww 1"
would you recommend this app friend? [[answer_choicess [0])),
(answer_choi [1]]], ((answer_choice) [2]]], [[answer_choices] (3])), or canswer_choices [4]]]*
Target Template:
( (answer fannseerchoices sstar-1]
Answer Choices Template:
167

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Not at all 111 No III Maybe |I| Yes
|||
Definitely
Prompt not for the original task intended by the dataset authors Input Template:
Generate a (sstar) -star review (1 being lowest and being highest) about an app with package (package_nanm name)
Target Template:
[zeview]] ) |
Prompt for the original task intended by the dataset authors Input Template:
what would be the -rating of this review beinn the lowest and being the highest)? "I "[revvex)]
Target Template:
[aasser http:eeeeehhiieseeeeeeee [star-1]]
Answer Choices Template:
111
11I
III
III
Prompt not for the original task intended by the dataset authors Input Template:
On scale of 1-5 (with 1 being least favorable and 5 being most favorable), how would you rate this review? "[[review)]"
Target Template:
[(star))
1.8.3 IMDB
Dataset from Maas et al. (2011). Used in training.
Data Example
168

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Key Value
text label
Bromwell High is cartoon comedy, It at the 8...
Prompts
Input Template:
The following movie review expresses what sent sentiment? ((text)
Target Template:
{ answer_choices [label] 1]
Answer Choices Template:
negative ||| positive
Input Template:
[text]]) Did the reviewer find this movie { "good or bad") { )
Target Template:
if answer_chote [label]
}}
Answer Choices Template:
bad
||| good
Input Template:
(textil Is this review || ['"positive or negative") ]
Target Template:
(answer_choicce [label] }
Answer Choices Template:
negative ||| positive
Input Template:
169

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[(text]] } How does the viewer feel about movic?
Target Template:
enswer_choicer [label] 1
Answer Choices Template:
negative ||| positive
Input Template:
(text]] F What sentimeri does the writer express for the movie?
Target Template:
[ unswer_choieee [label] }}
Answer Choices Template:
negative ||| positive
Input Template:
[text]] The sentiment expressed for the movie is
Target Template:
{{ answer_chot ces [1abel] }}
Answer Choices Template:
negative ||| positive
Input Template:
[text]] what is the sentiment expressed in this text?
Target Template:
{ anowor_choicos [label] 1}
Answer Choices Template:
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negative
III
positive
Prompt not for the original task intended by the dataset authors Input Template:
[text]]) This definitely not a
Target Template:
if answer enswer_choices ces [1-1abe11}> review.
Answer Choices Template:
negative ||| positive
Input Template:
[text]]] the reviewer enjoy the movie?
Target Template:
[ answer cchices [label] }}
Answer Choices Template:
No ||| Yes
Input Template:
[text]] What the sentiment expressed by the reviewer for the movie?
Target Template:
{ enswer_choicee [label] 1
Answer Choices Template:
negative ||| positive
Input Template:
[(text] I How does the reviewer feel about the movie?
171

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Target Template:
{
answer_ choices mnawer_choices
[1abel] 1]
Answer Choices Template:
They didn't like it! They loved it
1.8.4 ROTTEN,TOMATOR,
Dataset from Pang and Lee (2005). Used in training.
Data Example
Key
Value
the rock is destined to be the 21st century's new label
Prompts
Input Template:
[(tett]) Did reviewer find this movic {{ ("good or bad") } )
Target Template:
{ answer_ choices [label] }}
Answer Choices Template:
bad
||| good
Input Template:
[text]] what is the innt.. expressed in this text?
Target Template:
nnswer__hhoice [label] 1
Answer Choices Template:
negative ||| positive
172

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Input Template:
(text))
Is this review [("positive or negative")]
Target Template:
(answer choices [abel]
}
Answer Choices Template:
negative ||| positive
Input Template:
[teet))] Did the reviewer enjoy the movie?
Target Template:
if answer_choices [label] }}
Answer Choices Template:
No
||| Yes
Input Template:
[text)) How does the reviewer feel about the movie?
Target Template:
i( anower_choices [label] }}
Answer Choices Template:
They didn't like it III They loved it
Input Template:
[teett The centimert expressed for the movic is
Target Template:
173

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answer_choices [label] }
Answer Choices Template:
negative ||| positive
Input Template:
(text F1
What sentiment does the writer express for the movie?
Target Template:
{ answer_choicee [label]
Answer Choices Template:
negative ||| positive
Input Template:
The following movie review expresses what sent ientt? ((text) )
Target Template:
{ answor_choicos [label] }}
Answer Choices Template:
negative ||| positive
Input Template:
[teet)] | What is the sentiment expressed by the reviewer for the movie?
Target Template:
anawer_choices [label] }}
Answer Choices Template:
negative ||| positive
Input Template:
174

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[(text] How does the viewer feel about movic?
Target Template:
if anower_choices [label] }}
Answer Choices Template:
negative |||
positive
1.8.5
CEEP.REVVEW_PPEE FULL
Dataset from Zhang et al. (2015a). Used in training.
Data Example
Key
Value
label
text
dr. goldberg offers everything i look for in gen...
Prompts
Input Template:
:6 text 1}
So would like to give it
Target Template:
{ answer_choices [label]
Answer Choices Template:
star ||| stars |||
3 stars
|I| 4 stars
II|
5 stara
Input Template:
if text }}
Based that, my rating
Target Template:
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answer_choices [label]
Answer Choices Template:
star |1I stars
|II stars II1 stars |I|
5 stars
Input Template:
Review text: 16 text }]
Stars:
Target Template:
haswer_choices answer choices [label]
] }
Answer Choices Template:
star II| 2 stars III 3 stars ||| 4 stars
|I| 5 stars
Input Template:
{ text }} My rating for this place is
Target Template:
{ answer_choices [label] !
Answer Choices Template:
III stars
||| stars ||| 4 stars
5
Input Template:
Review text: 16 text 11
Review score (bstween. and 5)
Target Template:
i answer_choicas [label] 1
Answer Choices Template:
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111
111 111 111 5
Input Template:
Review: [[text]] 11 On scale of to 5, would give this product
Target Template:
i6 anawer_choices [labol] }
Answer Choices Template:
111
2
111
3
111
11I 5
Input Template:
Review text: 11 text }]
Review rating:
Target Template:
{
answer_ nhawer_choices ces [1abel]
Answer Choices Template:
star
|I| stars ||| stars |I| stars |I| 5 stars
1.9
SENTENCE COMPLETION
1.9.
SUPER_GLUS COPA
Dataset from Roemmele et al. (2011). Used in evaluation.
Data Example
Key
Value
choicel
The sun was rising.
choice2
The grass was cut.
idx
label
premise
My body cast shadow over the grass. cause
question
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Prompts
Input Template:
Exercise: choose the most plausible alternative.
if premise }) if question "cause" because... ($ else 8 so...
[$ endif }}
[(choicel])
[(choice2)]
Target Template:
[6 1f label != -1 $)1 answor_choicas [label] } ()( ($end15$)
Answer Choices Template:
[choicel]]] III [[choice2]]
Input Template:
18 if question == "effect" $
if premise }) What could happen next, "[[ answer_choices(0) }}" "(( answer jchoiercchices [1] }}"?
Target Template:
it label != -1 83 nuswer_choices [abel]] (endiis)
endif
Answer Choices Template:
[(choice]) III [cchoice2
Input Template:
[ premise H]
hesitating between two options. Help choose the more likely if question "cause" $ cause: else } effect: endif $)
[(choicel)) [(choice2)
Target Template:
18 label -1 8)11 answer_choices [label] } Sendif$)
Answer Choices Template:
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[choocel]]] 111 [cchoicc2]
Input Template:
if premi se }} if quest "cause" This happened because... eise 81 As consequence... (8 endif $ Help me pick the more plausible option:
[(choicel]] [(choice2))
Target Template:
label -1 {)11 answer_choicee [1abel] }} ($endi [$}
Answer Choices Template:
[(choicell] II1 [cchoice2)
Prompt from Schick and Schütze (2021) Input Template:
answer seswer_choicesse [C] }}"" "{( answer answer_choices [1] !!"? premise (8
question "cause" $) because ($ else 8) so (8 endis $)
Target Template:
{8 if label
1= -1 $ answer_choices [label] () endis 8)
Answer Choices Template:
[chhoieel III [choice2]]
Input Template:
16 if question "effect" $ if premise }) result, "(( answer_choicen [0] }1" or "((
answer_chee ces [1] })n?
Target Template:
if label !- -1 8)11 answer_choice ces [1abel] }} (Sendi =%)
endif $)
Answer Choices Template:
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[choocel]]] 111 [cchoice211
Input Template:
:f premi se }}
what's 's the best option?
[(choice]))
[(choice21)
We aze looking for 18 if question == "cause" $ cause {$ else $) an effect ($ endif $)
Target Template:
if label != -1 8) answer answer_choices label]]
[ } $endif8)
Answer Choices Template:
[(choice])] III [[choice2]]
Input Template:
13 if question == "cause" $
if premise }} Which may be caused by "[[ answer_choices(0) }}" or "{( nnswer_choices [1] }}"?
Target Template:
18 label != -1 8)11 answer_choices [label] } (endii5)
{3 endif $}
Answer Choices Template:
[(choice1)) III [cchoice2
Input Template:
Pick the more likely continuation to the following sentence:
if premi }) ($ question "cause" $) as a result of: (8 else $} as
consequence: [8 endi $)
[(choicel))
[(choice211
Target Template:
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label -1 $)11 answer_cholcos [label] } (endif$)
Answer Choices Template:
[(choice]] III [1choice2)]
Input Template:
(6 premise |1
select the most plausible question "cause" cause: {$ else effect: ($ endif $)
[(choicel]]
[(choice2))
Target Template:
label -1 {)11 answer_cho ces [1abel] ($endi =$}
Answer Choices Template:
[(choice]]) III [[choice2)]
Input Template:
$ 1f question == "cause" $1
premise }} Why? "! answer_cholccs(00) })* "(( answor_choices [1]
Target Template:
if label !- -1 $)11 answer_choices [label] } (Sendiff) #$)
endif $}
Answer Choices Template:
[(choice]) III [[choice2)}
Input Template:
{ premise 1) question "cause" because... (8 else $ so...
18 endif $1
Choose between:
[(choicel) [[choice2]
Target Template:
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label -1 $)11 answer_cholcos [label]
} $endif$)
Answer Choices Template:
[(choice]]) III [[choice2)}
1.9.2 HELLASWAG
Dataset from Zellers et al. (2019). Used in evaluation.
Data Example
Key
Value
activity_label ctx
Removing ice from car
Then, the man writes over the snow covering the wi... Then, the man writes over the snow covering the wi... then
ctx_b b
endings
I', the man adds wax to the windshield and cuts it...
ind
4
label
3
source_id
split
train
split_type
indomain
Prompts
Input Template:
Complete the description with appropriate enaing:
First, II lower. 1) Then, b. () H
(a) {( anawer_choices [0] H
(b) (( answer_choicer [1]
(c) (( anawer_choies [2] |]
(d) {{ answer_cholces [3]
Target Template:
{
answer answer_choices label int ().
Answer Choices Template:
(endings
join (" 11I ")}
Prompt for the original task intended by the dataset authors Input Template:
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What is the topic sentence: (fctx))
Target Template:
[(activity_label]
Prompt not for the original task intended by the datasct authors Input Template:
Complete the sentence: ((ctx)}
Target Template:
[canswer_choices| [abel int () H1
Answer Choices Template:
((endinss nga
join(" III ")
Prompt not for the original task intended by the dataset authors Input Template:
[[tt)]] ((endings [label | int (1)
Can you identify the topic of the paragraph?
Target Template:
(factivity_aabe
Input Template:
18 set prompts
*Can you pick the correct ending for the sentence: generate
"The task
ending for the sentence: 'How does this sentence end?
'From the list of endings described below, what ending makes the most sense for the sentence ',]
$1
(prompts choice))
(ctx))
(a)
([anawer_choloes/ }}
(b) ([answer_choices [1] }]
(c) (answer_choices 2
(d)
cnnsier_choioes [3]
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Target Template:
( [answer choices Senscer_choices
[label
int () 111
Answer Choices Template:
(endings
join (" 111 ")
}
Prompt not for the original task intended by the dataset authors Input Template:
16 set instance [C, 1, 2, choice $
Consider the following description: {f etx_a }}
Is the following an appropriate continuation? }
if ctx_b }} {{ Yes or No?
Target Template:
if 1abel instance string() $)
(answer choices [0]]]
else $1
(answer_core choices [1]]]
$ endif $}
Answer Choices Template:
Yes
|||
No
Input Template:
How does this sentence end? [cct]]]
(a)
Vanswer__hoicee [0]
(b)
((answer_choices [1]
(c)
([answer_choices
(d) [[anawer_choices [3]]
Hint: the topic of the sentence is Saativity_label label]
Target Template:
[anawer__hoices [label
int (}])
Answer Choices Template:
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(endings
join ("111"))
Prompt not for the original task intended by the dataset authors Input Template:
How would you start the sentence:
[endinga [label int ()))
Target Template:
(ctx)]
Prompt for the original task intended by the datasct authors Input Template:
:8 set instance [C, 1, 2, choice
Consider the following text: {{ ctx_b }) {f endings (instance) )
Is it an appropriate continuation of the following text: if ctx_a }}
Ves No?
Target Template:
if label instance string() $)
(answer choices [0]]]
else $1
fanswer__die choices chawer_chhoiessuie [1]]]
{ endif $}
Answer Choices Template:
Yes
|||
No
Prompt not for the original task intended by the datasct authors Input Template:
{ ctx b...
How does the description likely end?
Ending 1: {{ endings [0] F
Ending 2: {{ endings [1] }}
Ending 3: endings [2] H]
Ending ng 4: {{ endings [3] }}
Target Template:
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answer_choices [label
int ()]
Answer Choices Template:
Ending |I| Ending 2 III Ending III Ending
Input Template:
If description of a situation begins like this: {f ctx .)... Then how does it continue?
Ending 1: {f endings [C] }
kndingg ng 2: {{ endings [1] }}
knding 3: {{ endings [2] }}
Ending 4: endings [3] }
Target Template:
(answer_choi cas [1abe1 int ()] }}
Answer Choices Template:
Ending 1 |I| Ending 2 III Ending 3 III Ending 4
1.10
STRUCTURE To TEXT
1.10.1
COMMON GEN
Dataset from Lin al. (2020). Used in training.
Data Example
Key
Value
concept_set_idx
concepts target
l'ski', 'mountain', 'skier!) 1
Skier sk down the mountain
Prompts
Input Template:
Ignoring the order of the concepts: concepts join 1); Genezate a sentence with all the concepts
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Target Template:
[taarget) }
Input Template:
Put the concepts together to form sentence: {{ concepts | join (", ") 1).
Target Template:
[taarget)]
Input Template:
Construct sentence thth the word {{ concepts choi }
Hint: Use (concepts
join (", "}]} to restrict the output sentence.
Target Template:
(target |
Input Template:
{8 set seq
"From the concepts mentioned below, generate sentence: "Convert the concepts to a sentence:', 'Given the list of concepts, write a sentence:'
81 seq choice }}
concepts join ")
Target Template:
[target)) } }
Prompt not for the original task intended by the dataset authors Input Template:
What the topics in the sentence: [[target)
Target Template:
i concepts join (", ")
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Prompt not for the original task intended by the dataset authors Input Template:
We have the sentence: ((target
Extract all the key concepts:
Target Template:
{
concepts
join (", ") F
Prompt not for the original task intended by the dataset authors Input Template:
Can you write sentence about the topic /concepps choice)
Target Template:
[target)] }
Input Template:
Humans can easily string together rast concepts to form coherent sentence.
For example, with the concepts concepts join(", ") 11, simple sentence can
Target Template:
(target]
Input Template:
Given the list of concepts: {{ concepts | join(", ") });
Generate sentence with all the concepts
Target Template:
[targett] } }
1.10.2 WIKLBIO
Dataset from Lebret et al. (2016). Used in training.
Data Example
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Key
Value
inxut_text
('table': d'column_headen ['name', 'nationality'... walter extra is german award-winning aerobatic p...
target_text
Prompts
Input Template:
Facts:
16 for n in range (input_tex: ("table"" ["coouun_header] [length) 8)
i8 it input_text ""able""""" ("cooumm__eeaeeeeeeeee [n] I= aaticle_title $)
((imput_tex:) (eeext""tablee ["table"] ("coouuun @ecolumm_headeee [n] replace (" ") }]:
((input_tex) textt (ttablee" ["content"] H] {8 endif $}
18 endfor $)
Based these bullet points, write short biography describing the life of ((input_tex) ["context"]]].
Target Template:
[(target_text)
Prompt not for the original task intended by the dataset authors Input Template:
Read the bio below and try to give details on (iiput_texx: ["context"]]]
16 for in range (input_text ["tabla"] "eolumn_header/ [length) 8) ($ if
input_text ("txtle"tbler arrollmm__heddee [n] != Particle_titles $1 (( input_text ["table"] ] ("column_header" [n] [n] replace ("_"," "} }
{& endi $} (& endfor $
Bio: ([target_text))
Target Template:
18 for in range (irput_text ["table"] ["column_ ["column_header"" header") [length)
18 input_text, ["table"] ["col" ["column_header" [n] != pertcle_title" $) -(( input_text ["table"] "cclaamm_heeder") [n] replace _", ") }} is {f
16 endii ("aail""""""""""""""" ["content" " [n] H]
siput_text("tablee
endfor $1
Prompt not for the original task intended by the dataset authors Input Template:
What type of details about ((ipput_tox) ["context"]]] can be gathered from the following bio?
Bio: (target_texc
Target Template:
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for in range (input_text ["table"] ["column_headee (oan__h [lenghh) $ 16 If input_text (atxtt[aabbe ["table"] [""cc""""""""""""""" ["column_header" [n]] != "article_titll $)
{ input_text ["table"] ("column_header" [n] replacce ("_", "," ") }
18 endif $}
endfor
Prompt for the original task intended by the dataset authors Input Template:
18 for in range (irput_tex) ["table"] ["ccolum_headdr""]] [lngghh) $) 1& if input_text ["table"] [*column_hhe [""ooumm_heeddr"]]] [n] !- "article_title" and
input text "taabee" ""couumn____ headerr" [n] !=*name" $
smput_text|
iaput_tex: "table" (""ooomm_hhaaattteee" header" [n] replacee ("_,, ") 1 is (f ("content"" [n] |]
input_texe
18 endif $]
18 endfor $}
Given the details above, guess who could this information be about.
Target Template:
[1npuut_tex] ["context"]]
Prompt not for the original task intended by the dataset authors Input Template:
what key details about [input_teex ["context"]] *1)} be extracted from the following bio?
Bio: ((target_text))
Target Template:
13 for in range (irput_texx ["table"] ["columr_header""] |length) 8)
(8 if input_text ["table"] (rrolumm_headerr) [n] != "article_title $)
(( input_text ["table"] ("column__eader") [n] replace ("_", }} is
input_texts ("table"t ("cablle)"""cceee"""" ("content" ] [n] }]
18 endif $)
18 endfor $1
1.11 SUMMARIZATION
1.11.1 CNN_DAILYMAIL 3.0.0
Dataset from See et al. (2017). Used training.
Data Example
Prompts
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Key
Value
article highlights
It's official: U.S. President Barack Obama wants 1... Syrian official: Obama climbed to the top the t...
Input Template:
Can you write an outline of the following article in few points?
Article: ((article))
Target Template:
[(highlights)
Input Template:
Summarise the article: [(rticcee]]
Target Template:
[(highlights)
Input Template:
3 sentences, what are the main points one should remember from this news article?
Article:
[[article)]
Target Template:
[(igghlights]
Input Template:
Could you please generate TLDR (Too Long Didn't 't Read) summary of the following news article?
Article: ((article))
Target Template:
{ (thighlights)
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Input Template:
Condense the article down to the essentials to present it in the form of short cards in mobile apps:
[article]]]
Target Template:
(higghlightsl)
Prompt for the original task intended by the dataset authors Input Template:
Generate story from key plot points:
((igghlighss)
Target Template:
[article)]]
Input Template:
Sum the following article in brief: ((article))
Target Template:
[(highlights)
Input Template:
Extract koy points from the article based on which the stock market could react:
[article]]
Target Template:
[higghlights)
Prompt not for the original task intended by the dataset authors Input Template:
What details would you include ude in storyline to make it more engaging and informative?
((iighlights])
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Target Template:
[(artccle]]
1.11.2 GIGAWORD
Dataset from Graff et al. (2003). Used in training.
Data Example
Key
Value
document
australia 's current account deficit shrunk by a r... australian current account deficit narrows sharply
summary
Prompts
Input Template:
[document)
Generate title for this article:
Target Template:
ssummary
Prompt for the original task intended by the dataset authors Input Template:
Citle:
[unnnary)
Target Template:
[document]
Input Template:
Make a title for this article:
document
Target Template:
( ( sumnary)
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Input Template:
First sentence of the article: ((document)) }
Citle:
Target Template:
( a uumaayy }
Prompt from Radford et al. (2019)
Input Template:
[document]
TL; DR;
Target Template:
uumaary] }
Input Template:
( document] }
Given the above sentence, write its title:
Target Template:
(ssumary] }
Input Template:
Write title for this sentence: [(document) }
Fitle:
Target Template:
unmary!
Input Template:
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[document)} In a nutshell,
Target Template:
summary! 1
Prompt not for the original task intended by the datasct authors Input Template:
Fitle: ((aunnary)
Write an article with the given title:
Target Template:
{ (document)
1.11.3
Dataset from Fabbri et al. (2019). Used in training.
Data Example
Key
Value
document
National Archives Yes, it's that time again, ... The unemployment rate dropped to 8.28 last month...
summary
Prompts
Input Template:
:8 set docs document Sbcument.split ("aa2fffes$(3eeecthlaadde3111 1")
reject ("equalto", "*) list $} What are the key points across these news articles:
(8 for doc in does $)
Article: ((doc))
18 endfor $1
Target Template:
uummary 12:
Input Template:
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:8 set docs document split ('9o22fass223000ccchh5eeee111110 1*) reject ("equalto", "") 1ist $ Synthesize these documents into a single one: ; for doc in docs $)
{(doc)) endfor $
Target Template:
{ [u:mary(2:]]
Input Template:
16 set docs document. split ("cddhheellsssssbbaaseseeeel (*)
reject ("equalto", "") list $1
want to edit the following articles into a more concise summary:
18 for doc in docs $)
Article: ((doc))
{8 endfor $
Target Template:
(summarg ry ([:tmaaar[[]]]]]] [2:]]}
Input Template:
18 set docs = dncumentt.sli.. (bccmstttsppllsss222eeeeeeeeeee ("Falddacce(cceecchhhhaaeeeeel (*)
reject ("equalto", "") 1ist
Write summary of the following articles:
(6 for doc in docs $)
Document: [[doc))
i& endfor
Target Template:
{ (summary [2:1]}
Prompt for the original task intended by the dataset authors Input Template:
18 set docs document... .slitt (SSc2affce2ceeeeetttssseee111 1*)
reject ("equalto", "") list& write an expanded news article with plausible details From the fol following summary:
(summary
Target Template:
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(doos
choice)
}
Input Template:
:8 set docs = document. split ( @eoodffccce2044ccccccccccee0l110 (*) reject ("equalto", "*) list &
I'm trying to distill these articles down into
18 for doc docs 8
Article: ([doc] 16 endfor $}
Target Template:
( s summary [2:]1}
1.11.4 SAMSUM
Dataset from Gliwa et al. (2019). Used in training.
Data Example
Key
Value
dialogue
Amanda: baked cookies. Do you want some "emry........
id summary
13818513
Amanda baked cookies and will bring Jerry some tom...
Prompts
Input Template:
Summarize this dialogue: ( ((dialogue) }
Target Template:
unmaryy }
Input Template:
[dialogue)]
Given the above dialogue, write summary.
Target Template:
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summary]
Input Template:
Summarize: rize: ((dialogue)
Target Template:
( summary] }
Input Template:
[ddalogue]] 2o sum up this dialog:
Target Template:
(summary)) }
Input Template:
Generate summary for this dialogue: [dialogue]]
Target Template:
( summaay)) }
Prompt not for the original task intended by the datasct authors Input Template:
write dialogue that matches this summary ry: { (summary)]
Target Template:
[ddaaloue)
Input Template:
Sum up the following dialogue: [ddaalogue)
Target Template:
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summary] }
1.11.5 XSUM
Dataset from Narayan et al. (2018). Used in evaluation.
Data Example
Key
Value
document
Recent reports have linked some France-based playe...
id summary
29750031
New Welsh Rugby Union chairman Gareth Davies belie...
Prompts
Input Template:
( [document)
Mrite summary of the text above
Target Template:
[ssumaary)
Input Template:
Article: ( (document)) )
Summary:
Target Template:
(ssummar)) }
Prompt from Brockman (2020)
Input Template:
[document]
How would you rephrase that in few words?
Target Template:
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summary] }
Prompt from Brockman (2020)
Input Template:
My college roommate asked me what this article means:
(document)
So recapped it in layman' s terms:
Target Template:
uummary
Prompt from Brockman (2020)
Input Template:
[document]] 11
This boils down to the simple idea that
Target Template:
{ s uummary] }
Input Template:
Summarizes (document) }
Target Template:
[ssumaay]] }
Input Template:
Summazize this document: { document
Summazy:
Target Template:
( summary!
Input Template:
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[ddocmmnt]
ven the above document, write santence summar
Target Template:
{ s uumaary) }
Input Template:
First, please read the article below.
[document]]
Now, you write me extremely short abstract for it?
Target Template:
[uummaay) }
Prompt from Radford et al. (2019)
Input Template:
[doocuentt))
FL; DR:
Target Template:
s/s summary] }
1.12
TOPIC CLASSIFICATION
1.12.1
AGLNEWS
Dataset from Zhang et al. (2015b). Used in training.
Data Example
Key
Value
text
Wall St. Bears Claw Back Into the Black (Reuters)
label
2
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Prompts
Input Template:
What Label best describes this news article? [teett]
Target Template:
(answer_choices [label] }}
Answer Choices Template:
World politics III Sports
|||
Business
||| Science and technology
Input Template:
Is this piece of news regarding |I "world politics, sports, business, or science and technology" [text]]]
Target Template:
(answor_choicos [label] }
Answer Choices Template:
Morld politics II| Sports |||
Business ||| Science and technology
Input Template:
Would you recommend the following article to (("politician") ),, an { (("thheet")),, [(bbuiness executive")), or (("acientist")
if text }
Target Template:
(answer wttpseereehhiiessseeeeeeeee [abell]
Answer Choices Template:
Politician |I| Athlete |I| Business executive |I|
Scientist
Input Template:
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[(text]]
Which of the following sections of newspaper would this article likely appea in? [[Worll News"),, (("Sports))), {(*Business")), (["Science
and Technology")]
Target Template:
(answer choices [abel] }}
Answer Choices Template:
World News
||| Sports |I|
Business |||
Science and Technology
Input Template:
[text]]]
Which section of a rexspaper would this article likely appear in?
Target Template:
[[answer_choices [label]
}}
Answer Choices Template:
World News |||
Sports
|||
Business
||| Science and Technology
Input Template:
[ttet]]]
Is this piece of news regarding {[ (["wolld politics, sports, business, science and technology"!!?
Target Template:
(annserr_coo ces [1abe]] }}
Answer Choices Template:
World politics ||| Sports ||| Business ||| Science and technology
Input Template:
(textil
What label best describes this news article?
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Target Template:
[aanswer_choices [label] F
Answer Choices Template:
World politics III Sports
|||
Business
||| Science and technology
1.12.2 DBPEDIA.14 14
Dataset from Lehmann et al. (2015). Used in training.
Data Example
Key
Value
content
Abbott of Farnhan D Abbott Limited was Britis...
label
E. D. Abbott
Prompts
Input Template:
[content Given 1ist of categories: {"company, educational institution, artist, athlete, office holder, mean of transportation, building, natural place, village, animal, plant, album, film or written whrk"Hh, what category does the paragraph belong to?
Target Template:
answer answer_choices [label]
Answer Choices Template:
Company II| Educational Institution III Artist III Athlete III Office Holder II| Mean Of Transportation III Building
Natural Place III
Village III Animal III Plant III Album III Film III Written Work
Input Template:
Pick one category for the following text. The options are {I ""compnyy, educational institution, artist, athlete, office holder, mean of tzansportation, building, natural place, village, animal, plant, album, film or written work")). ((title)) {[content]]
Target Template:
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answer_choices [label]
Answer Choices Template:
Company III Educational Institution III Artist III Athlete III Office Holder II| Mean of Transportation III Building III Natural Place III Village III Animal 1I1 Plant III Album II1 Film III Written Work
Input Template:
[(titlell [content)] Given choice of categories [I [["comaany, educational institution, artist, athlete, office holder, mean of transportation, building, natural place, village, animal, plant, album, film or written work")), the text refers to which one?
Target Template:
{ answer_ l [1abe]]
Answer Choices Template:
Company III Educational Institution III Artist III Athlete III office Holder III Mean of Transportation III Building III Natural Place III
Village III Animal |I| Flant III Album III Film III Written Work
Input Template:
"((title))" H", given a list of categories: {I (""ompany, educational institution, artist, athlete, office holder, mean of transportation, building, natural place, village, animal, plant, album, film or written work"!lr what category does the title belong to?
Target Template:
if answer_chot ces [label] }
Answer Choices Template:
Company III Educational Institution III Artist III Athlete Office Holder II| Mean of Transportation III Building II] Natural Place III Village |I| Animal [I] Plant III Album III Film II] Written Work
1.12.3 TREC
Dataset from Li and Roth (2002). Used in training.
Data Example
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Key
Value
label-coarse
label-fine
text
How did serfdom develop in and then leave Russia ?
Prompts
Input Template:
Categories: (C', joio (answer_choices) }
What category best describes: ([text] Answer:
Target Template:
answer_choices [label_coarse]
Answer Choices Template:
Description III Entity III Location
Abbreviation
III
Person |II Quantity III
Prompt for the original task intended by the dataset authors Input Template:
18 set label_ label_mapping = (21:0, 18:1, 24:2, 11:3, 14:4) $} [3 if label_coarse == 8)
Is this question asking for (0', '.join (answer_choices) }}? [text]]]
Target Template:
answer_che civeer_coiiess ces [1abe1_mapping [1abe]] @aaal_finee }} endi:
Answer Choices Template:
city II| country
||| mountain III
state ||| other location
Prompt not for the original task intended by the dataset authors Input Template:
:8 label 1mpppng (39:0, 13:1, 8:2, 40:3, 25:4, 43:5, 27:6, 38:7, 35:8, 41:9, 32:10, 45:11, 14:12] $) $}
13 if label_coarse
[[text
Is this question asking for (t', j.join waw.weeeechiiceeeeeeeeee 1
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Target Template:
anawer cchicc_ Laapping (1abe1__1_1 }}
endif $}
Answer Choices Template:
period of time III pgrcentage III speed III temperature III size III weight III other number
code II| count III date III distance III price III order |II
Prompt for the original task intended by the dataset authors Input Template:
18 set label Sabbe__mappnng = (2:0, 22:1, 19:2, 1:3, 46:3, 23:4, 10:5, 17:6, 33:7, 37:8, 15:9, 30:10, 26:11, 16:12, 28:13, 42:14, 31:15, 20:16, 44:17, 36:18, 14:19) $}
[8 label_coaree
$1
Is this question asking for ((', '.join (answer_choices)) }}? [[text]]
Target Template:
[f answer choiceshhcceeeeeeee endif $1
[label_fire]] }}
Answer Choices Template:
animal III organ of the body III a color II| creative piece III eurrency III disease or medicine III event III food III musical instrument III language III letter III plant III product III religion || sport III substance II] symbol II| technique III term III vehicle word II| other entity
Prompt not for the original task intended by the dataset authors Input Template:
:8 label mapping (39:0, 13:1, 8:2, 40:3, 25:4, 43:5, 27:6, 38:7, 35:8, 41:9, 32:10, 45:11, 14:12) $1 18 if labcl_coarse = $1 Is this question asking for ((', J.join (answor_choocces)) }]? [(text))
Target Template:
anawer answer_choices choices [label_mappinn [label_fimne }}
endif
Answer Choices Template:
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code ||| count III date III distance III price III order |II period time III percentage III speed III temperature II| size ||| weight III
other number
Input Template:
Question: [[text]]
Descriptors: H4', j.join (answer (answer_choices) choices) }
Best Descriptor?
Target Template:
((answer_cho ces [label [aabel_coare]]
Answer Choices Template:
Description III Entity III Location
Abbreviation II] Person III Quantity III
Input Template:
[text]]]
What is this questior asking for?
Target Template:
(answer choices canicers llbbll fine]
Answer Choices Template:
Manner III Creative Piece III Animal III Expression abbreviated II| Individual 1II Group III Title III Defintion III Date IIl Reason III Event III state III Description III Count III Other III Letter III
Religion II| Food III Country III Color II1 Term III City II1 Organ of the body |I| Disease or medicine III Mountain III Price II1 Product III Period II| Substance III Sport II1 Plant III Technique II| Size III
Instrument !!| Abbreviation III Speed III Word III Language
Fercentage ||| Code III Distance III Temperature III Symbol II| Order
|| Vehicle |I| Weight II| Currency
Prompt not for the original task intended by the dataset authors Input Template:
18 set label labeingapping (21:0, 18:1, 24:2, 11:3, 14:4] &}
18 if laarr_ =- 8}
[[text]l
Is this question asking for (t', join choice) !!?
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Target Template:
anawer _chscr_chicess Saabel_mpping }} endif $1
Answer Choices Template:
city III country III mountain III state ||| other location ion
Input Template:
Which category best describes the following question: [[text]
Choose from the following list: 1(', '.join [
Target Template:
if anower_choices [labcl_coarsc] }}
Answer Choices Template:
Description III Entity III Location
Abbreviation
III Ferson
|I| Quantity II1
Prompt not for the original task intended by dataset authors Input Template:
18 set nabel_napping= 7:1, 12:0, 9:3) $)
14 1f label_coarss == $
Is this question asking for (C', jojoin (answer_choices)) }}? [[text]]
Target Template:
answer_cher ccor__hhhiie [1abel_ sabel_mappingieee [1abe]_fine]] }} endif $}
Answer Choices Template:
definition III description ||| manner of action II| reason
Input Template:
[text] F
Is this asking about (.."., (). join (aasssaw__.hhhoceee F1?
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Target Template:
{{ answer_ answer_choices [labe__carra
}}
Answer Choices Template:
Description III Entity III Location
Abbreviation
III
Person |||
Quantity 111
Prompt not for the original task intended by the datasct authors Input Template:
18 set label mapping= 3:11
18 label label_coarss coarse 8) Is this question asking for an ((', '.join (answer_choices) ) [(text]]
Target Template:
(answer choices [label mapppinmpppngggggaaa label fine]] endif
Answer Choices Template:
abbreviation
||| expression abbreviated
Prompt not for the original task intended by the dataset authors Input Template:
18 set label _eee__mpppngg_- (34:0, 3:1} 8)
13 if label_coarse == 8)
[teet]]]
Is this question asking for an (t', jojoin (ansseer_cchooc )
Target Template:
answer chorccsshhccssseee [label_mappile (eriingglaaae [label_fine]] }}
endif $
Answer Choices Template:
abbreviation
||| expression abbreviated
Input Template:
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Is the following question asking about if',
'ojoin hanswer_choiceee )
[text)
Target Template:
{ anower_cholces [label_coarsc]
}}
Answer Choices Template:
Description III Ectity III Location
Abbreviation
II| Ferson
|II Quantity III
Prompt for the original task intended by the datasct authors Input Template:
18 set label_mee Saabe_g = (5:0, 4:1, 6:2, 12:3) 8) 18 if label_coarse == $
Is this question asking for (0', '.join (answer_choices) F1? [(text]]
Target Template:
answer choices stabel_mapping [label_fine]] }} ondif $}
Answer Choices Template:
group III individual III title |||
doscription
Input Template:
What is this question asking for?
[teett]]
Target Template:
if answer_choice laabel_finee }}
Answer Choices Template:
Manner II| Croative Piece III Animal III Expression abbroviated III
Individual 111 Group III Title III Defintion III Date III Roason III
Event III State III Description III Count III Other III Letter III
Religion II| Food III Country III Color II1 Term III City 111 Organ
the body II| Disease or medicine III Mountain III Price 111 Product III
Period |I| Substance III Sport II1 Plant III Technique que II| Size III
Instrument [I] Abbreviation III Speed III Word III Language II|
Percentage ||| Code III Distance
Temperature III Symbol Order
Il Vehicle II| Weight ||] Currency
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Prompt for the original task intended by the dataset authors Input Template:
[6 set lappi_g = (5:0, 4:1, 6:2, 12:3) 8) 18 1f label_coarse = $1
[text)]]
Is this question asking for ((', '.join (answer_choices) }
Target Template:
if answer_cho ces [1abel_mappl [1 [1aee__ffndd {) endi $)
Answer Choices Template:
group III individual III title |||
description
Prompt not for the original task intended by the dataset authors Input Template:
16 set Label_ mapping=02) 7:1, 12:0, 9:3) 18 Label_coarse [text]]]
is this question asking for ((', ".join }}?
Target Template:
{f answer__hooeee choices [label_mapping [label_fine]] }}
(8 endif $1
Answer Choices Template:
definition ||| description ||| manner of action |||
reason
1.13
WORD SENSE DISAMBIGUATION
1.13.1
SUPER GLUE WIC
Dataset from Pilehvar and os's e Camacho-Collados (2018). Used in evaluation.
Data Example
Prompts
Input Template:
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Key
Value
endl
36
end2
32
idx
label
0
sentencel
Do you want to come over to my place later?
sentence2
political system with place for the less prem...
startl
21
start2
27
word
place
Does the word "I "[[word]] ) have the same meaning in these two sentences? Yes, No? [sentennee]]
(sentence2))
Target Template:
18 if label !- -18) (answer_choff [1abel]]3 endif
Answer Choices Template:
No
|||
Yes
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Does the word [[word )" have the same meaning in these two sentences? [sentencell] [sentencee]]
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18 if label != -18) (answer_choicee [label]}} }} endif $)
Answer Choices Template:
No
|||
Yes
Input Template:
Homework
Deci de whether the word "[(wodd]) ) is used with the same meaning in the two following sentences. Answer by yes or no. [sentencel]]
[sentence2]]
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Target Template:
18 it Label != -18) ([answe_chooiee [label]]} endif $}
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No
|||
Yes
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Sentence [Senteecce]) }
Sentence sentencee]]
(( ([wod) has similar meaning in sentences à and B. True or False?
Target Template:
18 if label 1= -18] (answer_choicee [label]]} endif $}
Answer Choices Template:
False
|||
True
Prompt from Brown al. (2020)
Input Template:
(sentencel] }}
[senteence]]
Question: Is the word ([word) ! used in the same sense in the two sentences above?
Target Template:
if label != -1%] (answer choices [label]] }} endif $}
Answer Choices Template:
No
|||
Yes
Input Template:
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Sentence [ssentencee] } Sentence 2: [1sentenco2]
Determine whether the word "( "((word))" is used in the same sense in both sentencea. Yes no?
Target Template:
it label != -1 (answer_choi cas [labell)} endif $}
Answer Choices Template:
No
|||
Yes
Input Template:
Determine if the word [[word)] }' used the way in the two sentences below.
[sentencell] [sentence2]]
Target Template:
18 if label != -18)
canswer_choices choices [label]] }}
endif
Answer Choices Template:
No
|||
Yes
Prompt from Brown al. (2020)
Input Template:
(sentencel]) |
[Senteecc2]]
Question: Is the word ' ([word)) } used in the same sense in the two sentences above? Yes, No?
Target Template:
18 if label != -1%) (answer_choieee choices [label] }}
endif
Answer Choices Template:
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No ||| Yes
Input Template:
The word "[ (word) ) has multiple meanings. Does it have the same m in sentences 1 and 23 Yes or no?
Sentence [[sentercel]] Sentence 2: [[sentenco2]
Target Template:
label -1
[aanserr_hhiiee choices [label]] }}
& endi $}
Answer Choices Template:
No
|||
Yes
Prompt from Input Template:
[seettnnce]] }
[sentence2]]
Similas
{ [word]] }
Target Template:
{8 if label !- -18)
(answer choices [labell)) }) endif
Answer Choices Template:
No
|||
Yes
216


Reviews:
[{"review_id": "hjD3gTKK9MI", "reviewer": "Reviewer_Ju6C", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Large pre-trained language models demonstrate state-of-the-art performance with large amounts on labeled training data. This has inspired a line of research to demonstrate generalizability of the models when trained with few labeled training samples. Notably there is a huge performance gap between fully supervised SOTA models like BERT, and few-shot and zero-shot learning with models like GPT-3. In this work, the authors aim to use labeled training data from a large number of tasks to demonstrate the improvement of model performance on unseen tasks. To this end, the authors leverage prompting to convert the format of all the tasks and their labels to an unified format for multi-task learning. \n\nThe idea of converting all the tasks to an unified format is not new, and has been explored in earlier works like DecaNLP, UFO-Entail, T5 and more recently in the CLUES few-shot benchmark. One of the primary contributions of the work is in aggregating resources across 12 tasks, 54 datasets and a large number of prompts for converting these tasks to the unified format. This is a quite valuable resource for the community.\n\nNow, coming back to the experiments, the authors partition the task types into training and hold-out a subset of the tasks, including all the datasets therein, for evaluation. The authors perform an important study to verify that data for those tasks is not leaked through the pretraining corpus which addresses memorization concerns. However, this analysis is missing for the training tasks. Since the objective of this work, is to evaluate \"true\" zero-shot performance of the models, it is necessary to perform this analysis to ensure that any text for the \"unseen\" tasks and datasets is not leaked from the training corpus. This is important since it is often difficult to explicitly partition tasks into disjoint sets. For instance, the authors evaluate their models on held-out NLI tasks, but train their models on paraphrase identification tasks that bear some similarity with the entailment tasks. Can the authors report the multi-task model performance on NLI tasks without using the paraphrase identification tasks?\n\nThe authors compare the performance of their model and demonstrate the multi-task version (T0) to perform better than the single-task version (T5-LM) and outperform GPT-3 in primarily NLI tasks. The performance in WiC seems a bit strange given that random choice should give an accuracy of 50%.\n\nFinally, some analysis on transferability of the tasks, to study the impact of different source tasks on target tasks, will be an interesting contribution for the multi-task setting.\n", "summary_of_the_paper": "The authors demonstrate that massive multi-task learning with prompting can improve generalizability of large language models for zero-shot inference on unseen tasks. \n", "summary_of_the_review": "The authors demonstrate that massive multi-task learning with prompting can improve generalizability of large language models for zero-shot inference on unseen tasks. The authors develop a very useful resource for the community aggregating prompts across several tasks and datasets. The claim of \"true\" zero-shot generalization needs to be further supported by two more experiments as highlighted in the main review, namely, (i) analyzing the overlap in text from the MTL setup with that of the unseen tasks, and (ii) ablating paraphrase identification tasks to find the impact on NLI tasks.\n", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "ISHdtkLT6SX", "reviewer": "Reviewer_qyUt", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "There are some major comments which are presented here, \n\n1- why MNLI and QNLI are not considered in NLI tasks?\n\n2- Why yellow-colored tasks in figure 2 are not considered as unseen tasks too? In other words, why different held-out sets are not considered for unseen tasks? \n\n3- The authors mentions that humans are not explicitly trained on NLI tasks, story completion, coreference resolution, or word sense disambiguation, and for that reason, these tasks are selected as held-out to test zero-shot generalization. For the same reason, question answering or paraphrasing can be selected, since humans are not explicitly trained on these tasks!\n\n4- Section 5: the author mentioned training encoder-decoder model is computationally efficient since it is  trained to only generate the target as opposed to GPT3. However, isn't the  model use more supervision to generate the input as well, which can benefit the zero-shot performance? later, the author mentioned that since T5 is pretrained with masked language modeling, which is different than conditional text generation, they used T5LM model, the language model adapted T5. Isn't this contradict their previous statement about benefits of target-only generation?\n\n5- how about T5 performance with different sizes?\n\n6- it is mentioned that the best checkpoint was selected from step 12200 since it yields best validation metric. However, the training set of T0, T0+ and T0++ are different. are they using the same validation with same tasks?\n\n7- Figure 4: why T0LM is not trained on the training mixture dataset? it is mostly underperforms T0\n\n8- author mentions that they did not do prompt selection using best evaluation performance. Since zero-shot tasks are different than training, don't they have a completely different prompt template?\n\n9- Since T0 model is the finetuned T5 on multi-tasked prompted dataset, I think the author should also add a finetuned encoder-decoder model which is not using T5 pretrained weights as initialization. This will helps to understand the benefits of encoder-decoder training for zero-shot learning. This way, the T0 can be evaluated on more BigBench tasks which has not in-vocabulary T5 tokens. Moreover, using pretrained T5 model for T0 means using implicit multi-tasking trained during T5 pretraining.\n\n10- it would be interesting to see the T0 performance which trained with different number of mixed training tasks. In other words, what is the impact of each training task, to zero-shot learning\n\n11- T0 outperforms 7 out of 11 zero-shot datasets, but it outperforms on 2 out of 4 zero-shot tasks. It underperforms on all datasets of  story completion and coreference resolution. what is the explanation for this?\n\n12- what is the purpose of evaluating T5LM without training on multi-tasked training dataset in Figure 4 ? it underpreforms Gpt3 (6.7B) on all tasks except WiC!\n\n13- the contribution is not clear. if it is about the benefit of encoder-decoder  model to decoder-only LM models for zero-shot learning, then a decoder-only LM model should be trained on the multi-tasked prompted training set. if the contribution is about the proposed multi-tasked prompted dataset\n\n14- Figure 5: there is no constant improvement between T0, to T0+ and T0++. In other words, adding more dataset to multi-tasked trained does not always improve zero-shot learning on BigBench. what is the justification for this behaviour of encoder-decoder model? if using more training data sometimes reduce performance, e.g. Known Unknown and Logical Deduction tasks, perhaps due to model-scale constraint, it would be helpful to evaluate with a larger T0. In other words, what is the relation between T0 model scale and training size to the zero-shot performance\n\n15- Section 6.2: the author compared T0 with GPT3 on RTE tasks with different prompts. what is the reason for just choosing RTE for this comparison only? shouldn't this comparison be done on all zero-shot tasks of Figure 4, to evaluate the robustness of GPT3 to prompts as well? \n\n16- The performance of T0 can also be compared with FLAN model in Figure 4 for each zero-shot task, despite different training set.\n\n17- Section 7: the author mentions a key difference between T0 and FLAN model is that T0 is an encoder-decoder model which is pretrained and finetuned with different objectives. However, no clear comparison with FLAN model is presented in the paper \n\n18- Section 7: author mentioned that FLAN model with comparable size to T0 (8B) finds that increasing multi-task training reduce zero-shot performance, whereas they find the opposite. The only evaluation on the size of multi-task training dataset is presented on BigBench dataset in Figure 5, which is different than FLAN zero-shot evaluation. ", "summary_of_the_paper": "This paper proposed a new method for zero-shot generalization of NLP models. Since zero-shot generalization of the unsupervised pretrained language models are understood as an ability which is captured **implicitly** during unsupervised training on large natural text, this paper suggested a supervised learning method to understand zero-shot generalization through **explicit multi-task learning** in NLP models. To facilitate the multi-task supervised learning, each task is converted to multiple prompts, and an encoder-decoder model is trained (T5 model), to generate the answer for each tasks. The paper also provided a collection of crowdsourced prompt formats for each dataset, called P3.\nThe results indicate that a model which is trained with multiple prompts per dataset is less sensitive to the wording choice of prompts. The results also show that using encoder-decoder model which is trained to only generates the target, is more computationally efficient than standard language modeling training. It is also shown that the model is ~16x smaller than GPT3 and can attain similar performance in zero-shot learning. ", "summary_of_the_review": "This paper presents a new method for NLP zero-shot learning using supervised multi-task prompted training. They proposed to use encoder-decoder model which is already pretrained using masked language modeling to attain better zero-shot performance with smaller model compared to large-scale autoregressive language model (GPT3). However, there are some shortcomings,\n\n1- The zero-shot evaluations are based on some selected tasks, including NLI, story completion, word sense disambiguation, and coreference resolution. In this setting, other tasks such as paraphrasing, QA, sentiment analysis, summarization, topic classification are not considered as zero-shot tasks, and only used for training, which makes an incomplete evaluation for zero-shot learning.\n\n2- The author mentioned the benefits of encoder-decoder pretraining objective (target-only generation) to auto-regressive objective (input generation) as a key factor. However, there are no comparison with auto-regressive model with the same pretraining and zero-shot task evaluation. The only comparison is on the BigBench dataset, where Transformer-based language models are pretrained with different dataset, and also performance comparison on NLI, story completion, word sense disambiguation, and coreference resolution.\n\n3- The paper is centered around understanding the multi-task pretraining, using explicit supervised learning of model. However, the evaluated model (T0) is using pretrained weights of T5 for training, which implicitly learned multi-tasking during unsupervised pretraining of T5 model. This makes the evaluation biased and an unfair comparison to GPT3 model.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "G1PXySNYxxO", "reviewer": "Reviewer_n8Ae", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Strengths:\n\n1. This paper asks two very important questions about applying LMs, and since LMs are so widely used right now, I believe investigating these questions is super important to the community right now.\n2. The authors ran a lot of experiments, and while I did point out some missing potential experiments in the weaknesses section, I would like to state that overall the set of experiments ran by the authors here is very strong.\n3. The paper is well-written and easy to follow. \n4. The authors shine a light on the importance of creative and diverse prompts, some of which may seem too verbose or even silly at first. I really like this direction and hope this paper leads to this research direction being further explored!\n\nWeaknesses:\n\n1. I feel like your main baseline is the T5+LM model (T5 finetuned on the causal LMing task). Therefore I think it would make the paper much stronger if you were to show how that baseline performs on the tasks in Figure 5.  I understand that the baselines that you use in Figure 5 were built and trained by an external third party, so I trust that these baselines aren’t too weak or unoptimzed somehow, but it would still be very important to show that you also improve over T5 in this setting.  It would also be very interesting to see how well GPT-3 performs on those tasks, but I did not and will not penalize you for not having those numbers, as the GPT-3 API is not fully open and free.\n2. This paper seems like an extension of ideas discussed in iPET/PET (“It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners”, Schick & Schutze), and so to me it feels like it would be quite important to compare to that model in at least some of the experiments that you run, for example, if Figure 4 compared to iPET I feel the paper would be much stronger. What do you think? Are T0 and iPET comparable? \n\nQuestions/comments:\n\n1. Could you explain and/or cite relevant source “GPT-3 is behaviorally capable of zero-shot generalization to new tasks” (section 2) in the next draft?. I know that later on in the paper you do talk more about this, but I think it might also be important to explain this claim in section 2. I think that many people believe that while GPT-3 wasn’t explicitly trained on tasks such as translation or adding numbers,  GPT-3 can only do well on “zero-shot” tasks that it has implicitly observed during training\n2. I’m using the chrome PDF reader and do not see an actual figure above the caption for figure 3. \n3. “Unlike decoder-only language models such as GPT-3, is not trained to generate the input. This has computational benefits since in our prompted data, the target typically is much shorter than the input”. You might not need to generate the input but you still need to encode it and so you only save the time taken on the softmax layer which is insignificant. In addition, since you use an enc-dec model you need those extra encoder-attention sublayers so it’s not so clear that there’s an efficiency advantage here. I would like to ask you to consider adding some actual experiments that show that this claim is correct, or cite a relevant paper, or just remove that sentence. \n4. “Baseline models are standard Transformer-based language models with varying size.” (Figure 5). What is a ‘standard transformer LM’? I don’t think such a thing really exists. I think it could be helpful in the paper if you would give more details, such as: position embedding, FF dimension, number of heads, head dimension, and number of layers. \n5. Make sure Figure 5 is explicitly referenced in the text in the relevant paragraph. \n6. Consider making the following statement slightly weaker: “These results suggest that T0 is more robust to prompt formulation than GPT-3”. It’s hard to draw such a strong conclusion from an experiment on one dataset. \n7. In figure 6, what is the meaning of each dot? Is it the performance for a given prompt? If so, that should be explained in the caption.\n", "summary_of_the_paper": "Today, most NLP applications use language models. These language models can be split into two different categories. The first is the GPT-3-style models, that are only trained on the unsupervised language modeling objective. In the second category, we have the models that use the finetuning method that was popularized by ELMO and BERT, where the model is first trained on an unsupervised objective and is then trained on supervised tasks. \n\nThis paper asks two questions about models in the second category. Specifically, the authors take the T5 model and test if it improves performance on unseen tasks when finetuned on multiple other tasks. The second question is whether training on more prompts improves performance. \n\nThe authors show that their model does indeed improve performance on multiple held-out datasets, when doing multitask training. In addition, the authors show that the median performance improves when training on multiple different prompts (and testing on unseen prompts). ", "summary_of_the_review": "The authors ask two very interesting research questions with regards to prompting and finetuning of large LMs. A wide array of experiments answers these questions, and I think this will lead to better usage of LMs in the future. I did point out that there are some baselines that the authors missed, but I don’t think that that’s a big deal. I recommend that this paper be accepted, and feel that it will be very valuable to the NLP community. \n", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "4: The contributions are significant, and do not exist in prior works."}, "extra_text": {}}, {"review_id": "5MCNU1KR4hx", "reviewer": "Reviewer_zBjM", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Overall, this is a conceptually simple and clean paper with extensive results.\n\nStrengths\nThe tasks considered are extensive, covering many areas of NLP tasks such as QA, NLI, sentiment classification, summarization etc. The zero-shot evaluation tasks are of different categories than in the training set, which helps support the zero-shot generalizability claim.\n\n\nWeaknesses\n- One could say that the paper lacks technical novelty, but to me, a simple paper with extensive results that work is a better than a technically rich paper that doesn't work as well.\n- In a way, we can see the multi tasks as a single giant task with many domains. If we view this as question answering, the paper is essentially training a system that learns to answer well given various domains of questions/tasks. What would also be interesting is to see/what's missing in the paper, is to carefully study the difference in domains of the training tasks, versus zero-shot evaluation tasks. Repeated experiments with different subsets of training/eval tasks can help shed some light as well. However, I do understand that it would be an empirically expensive set of experiments.\n- The conclusion of this paper is that multi-task supervised learning makes the model generalize well. However, we still rely on a vast amount of supervised data. In a way, the comparison with GPT-3 is not too fair.\n\n\nQuestions\nCan authors provide intuition about the performance of T0 vs FLAN on the datasets that T0 does better, or worse? Do it mainly have to do with auto-regressive style versus encoder-decoder (that T0 does not predict the input)? ", "summary_of_the_paper": "This paper trains a sequence to sequence model in a large multi-task setting and tests the model's ability to generalize to unseen tasks (zero-shot). The crux of the contribution lies in the design of prompt setups, extensive experiment, and the evaluation. The conclusive of this paper supports a growing trend/consensus in the community that multi-task learning can be a good way for generalizability on unseen tasks. ", "summary_of_the_review": "This is a solid paper in terms of contribution to the language modeling line of work. It will steer the community in the right direction of doing multi-task learning as a way to meta-learn new task or adapt to unseen tasks. ", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper studies constructing text2text transformer models that are good at zero-shot task generalization via multi-task learning over a diverse set of NLP tasks. One main contribution of the work is to create prompt templates for various NLP tasks (that are of different task formats) such that all tasks can be framed into text2text learning format and that is "natural" to the pretrained T6 model. The paper conducts extensive experiments to demonstrate the promising zero-shot generalization ability of such multi-task learner.

Strength:
- Important problem setup that has broad applications
- Extensive experiments to validate the claims
- Useful resources are developed for the problem

Weakness:
- Good to study the effect of using different combination of training tasks on the downstream zero-shot generalization, which can shed some light on the usefulness of upstream tasks
- Justification of "true zero shot learning" capability would require further experiments on analyzing the data overlap between MTL datasets (and also T5 pertaining task data) and the unseen task data.
- Some more discussion on the task split and categorization will be helpful.
Decision: Accept (Spotlight)