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tasksource: A Dataset Harmonization Framework for
Streamlined NLP Multi-Task Learning and Evaluation

Damien Sileo

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Damien Sileo. tasksource: A Dataset Harmonization Framework for Streamlined NLP Multi-Task Learning and Evaluation. 2023. hal-04099649

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Distributed under a Creative Commons Attribution 4.0 International License
tasksource: A Dataset Harmonization Framework
for Streamlined NLP Multi-Task Learning and Evaluation

Damien Sileo<fnref n="1" />

¹Univ. Lille, Inria, CNRS, Centrale Lille, UMR 9189 - CRISTAL, F-59000 Lille, France
damien.sileo@inria.fr

Abstract

The HuggingFace Datasets Hub hosts thousands of datasets, offering exciting opportunities for language model training and evaluation. However, datasets for a specific task type often have different schemas, making harmonization challenging<fnref n="1" />. Multi-task training or evaluation necessitates manual work to fit data into task templates. Several initiatives independently tackle this issue by releasing harmonized datasets or providing harmonization codes to preprocess datasets into a consistent format. We identify patterns across previous preprocessing efforts, such as column name mapping and extracting specific sub-fields from structured data in a column. We then propose a structured annotation framework that ensures our annotations are fully exposed and not hidden within unstructured code. We release a dataset annotation framework and dataset annotations for more than 500 English tasks<fnref n="2" />. These annotations include metadata, such as the names of columns to be used as input or labels for all datasets, which can save time for future dataset preprocessing, regardless of whether our framework is utilized. We fine-tune a multi-task text encoder on all tasksource tasks, outperforming every publicly available text encoder of comparable size in an external evaluation<fnref n="3" />.

# 1 Introduction

Datasets are a key ingredient in modern artificial
natural language processing (NLP).

Language understanding models trained on unan-
notated corpora need to be evaluated, and individ-
ual datasets or benchmarks with multiple datasets
provide an objective measure of targeted model
capabilities. Supervised fine-tuning on annotated
datasets also leads to better evaluations, and multi-
task learning (MTL) (Caruana, 1993) or extreme

MTL (Aribandi et al., 2022), i.e. MTL with many
tasks, improves robustness.

The HuggingFace Datasets (Wolf et al., 2020) Hub hosts thousands of datasets. However, running evaluations or MTL on many datasets requires manual work because of a lack of standardization. Fine-tuning a model on multiple datasets requires alignment of datasets formats, even with a single task type (e.g. natural language inference). Because of that, various initiatives assemble datasets or preprocessing code to ease multi-task learning or benchmarking. However, they either distribute prepossessed copies of the datasets or preprocessing code associated with each dataset. Section 2 enumerates previous works enacting these two approaches.

The previous preprocessing codes implicitly use
some metadata, such as mappings between col-
umn names and fields of a task, but extracting it is
quite difficult. Code is not disentangled from meta-
data. We propose a very concise dataset annotation
format by relying on patterns reoccurring across
several preprocessings. Most annotations fit in a
single line, e.g:

scitail = Classification(
  'sentence1',
  'sentence2',
  'gold_label')

The SciTail (Khot et al., 2018) dataset on HF-Hub, noted *scitail_ds*⁴ (Khot et al., 2018) can be standardized by calling the `tasksource.scitail` function, and associated metadata can be retrieved with `tasksource.scitail.dict()`.

We annotate 480 tasks, focusing on discrimi-
native tasks to complement previous work better.
We train a deberta-base text encoder on all
of them simultaneously (Section 6) leading to un-
precedented model performance (Section 7).

arXiv:2301.05948v3 [cs.CL] 16 May 2023

<footnote n="1">¹https://xkcd.com/927/</footnote>

<footnote n="2">²https://github.com/sileod/tasksource</footnote>

<footnote n="3">³hf.co/sileod/deberta-v3-base-tasksource-nli⁴https://hf.co/datasets/scitail</footnote>
## 2 Related work

Various works harmonize existing datasets by either sharing preprocessed copies or preprocessings. Tasksource is a collection of preprocessings, and it is the largest for tasks excluding text generation tasks. Text generations tasks have a relatively simple format (optional input text, and output text), and previous work such as PromptSource (Bach et al., 2022) and SuperNatural Instructions (Wang et al., 2022) did not provide structured annotations, as defined in section 3, but these can still be combined with acceptable efforts.

**Preprocessed copies** BIG-Bench (Srivastava et al., 2022a), BigBio (Fries et al., 2022), Natural and SuperNatural Instructions (Mishra et al., 2022; Wang et al., 2022), PragmEval (Sileo et al., 2022a), UnifiedQA (Khashabi et al., 2020b), Tweet-Eval (Barbieri et al., 2020), DiscoEval (Chen et al., 2019b), Silicone (Chapuis et al., 2020), LexGLUE (Chalkidis et al., 2022), SetFit (Tunstall et al., 2022) distribute preprocessed copies of the original data with standardized format.

**Collections of preprocessings** SentEval (Conneau et al., 2017), Jiant (Pruksachatkun et al., 2020), BLUE (Peng et al., 2019), MetaEval (Sileo and Moens, 2022a), CrossFit (Ye et al., 2021), PromptSource (Bach et al., 2022) distribute the code required to jointly use some datasets with initially distinct structures. ExMix (Aribandi et al., 2022) is not released to our knowledge. The Muppet (Aghajanyan et al., 2021) authors did not release their preprocessing either.

Our work also pertains to extreme MTL (Aribandi et al., 2022; Aghajanyan et al., 2021) and dataset count scaling.

## 3 Structured dataset annotation

We define dataset parsing as the mapping of a dataset into a task template.

A task template is a type of task, like paraphrase detection, associated with a predetermined set of fields. For example, Paraphrase detection can be mapped to a task template `PARAPHRASEDETECTION(SENTENCE1, SENTENCE2, LABEL)`

A dataset is a set of examples with named and typed columns. `quora` is an example of a dataset hosted on the HuggingFace Datasets Hub (Wolf et al., 2020), illustrated in Table 1.

A dataset parser for a specific dataset is a function that maps the whole dataset, or examples, to

<table><caption>Table 1: One row of the Quora dataset, as hosted on the HuggingFace Datasets Hub.</caption><thead><tr><th>questions (sequence)</th><th>is duplicate (bool)</th></tr></thead><tbody><tr><td>{ "id": [ 1, 2 ], "text": [ "What is the step by step guide to invest in share market in india?", "What is the step by step guide to invest in share market?" ] }</td><td>false</td></tr><tr><td>...</td><td></td></tr></tbody></table>

a task format, which can be PARAPHRASEDETECTION here.

As seen above, some benchmarks distribute harmonized datasets. This approach can save computations but can waste storage space, and make it harder to track all the design decisions that were applied to the original dataset. Users can also implement parsers themselves, or rely on external libraries to process examples on a restricted set of tasks. The previous preprocessings codes do not disentangle data and logic, and cannot be seen as semantic dataset annotations. This complicates the combinations of different preprocessing. Previous preprocessings also contain repetitive boilerplate code<fnref n="5" />.

We decompose dataset parsing logic from annotations with based on two observations:

(1) The fields of task type (e.g. SENTENCE1, SENTENCE2 for PARAPHRASEDETECTION can often be independently mapped to functions of dataset examples. Therefore, we can annotate a dataset with a task type, then annotate each field of a task type with a function that extracts the desired information from a dataset example.

(2) The field mapping functions are often selecting a column from examples: in that case, they can be annotated with the name of the relevant columns. Sometimes, as in the Quora dataset in Table 1, they are selecting a path from a nested structure: in that case, they can be annotated with a path. Fields can also be mapped to a constant – some multiple-choice question-answering datasets always use the first choice as the correct choice and have an implicit constant label equal to 0. A field can also be mapped to a concatenation of the text of different columns, which can also be annotated

<footnote n="5">⁵i.e. https://github.com/INK-USC/CrossFit/blob/master/tasks/aqua_rat.py</footnote>
with parameters.

## 4 Tasksource dataset annotation format

Once a dataset is annotated with a task type, each field of the task type has to be annotated with a function that takes an example from the dataset and returns the intended part of the example. For brevity, we can annotate a field with a string `s` to denote the function `lambda x:x[s]`

The tasksource backend handles the annotations and turns them into harmonizing preprocessing. We consider 3 general task types:

**CLASSIFICATION**(*TEXT1*, *TEXT2*, *LABELS*) where *LABELS* has to be a function that takes an example and returns a class index. It can also return a float for regression tasks, or a fixed-size list for multi-label classification. *TEXT1* takes a dataset example as input and returns the text extracted from the example. *TEXT2* is optional and is here to leverage the fact that most text encoders process text pairs with special care.

**MULTIPLECHOICE**(*PROMPT*, *CHOICES*, *LABELS*): *CHOICES* has to be a function that returns a list of text choices (the number of choices can differ across examples) extracted from an example. For concision, it can also be a list of column names to denote a list of textual choices already available in the example. *LABELS* has to return the index of the correct choice (most tasks have only one correct answer).

**TOKENCLASSIFICATION**(*TOKENS*, *LABELS*) where *TOKENS* takes an example as input and returns to a list of already split tokens, *LABELS* return a list of labels aligned to the tokens ($i^{th}$ label annotates the $i^{th}$ token).

We also provide 3 structured function factories to cover additional use cases while exposing their behavior with parameters.

**get** enables to access nested objects. `get.questions.text[0]` is equivalent to `lambda x:x['questions'][ 'text ][0]`

**constant** provides constant functions. `constant(x)` is equivalent to `lambda*_:x.`

**cat** concatenates multiple columns that contain strings. `cat(col1 col2)` is equivalent to `lambda x:x[ col1 ]+x[ col2 ]`.

An annotation to parse the Quora dataset in Table 1 can then be written as follows:

```
quora = Classification(
    text1 = get.questions.text[0],
    text2 = get.questions.text[1],
    labels = 'is_duplicate')
```

For completeness, we also allow optional `preprocess` and `postprocess` arguments to a task type. They should be functions that take the full dataset as input and return a dataset. We found this feature to be necessary in a few cases where datasets had unusable labels (e.g. negative label indexes) that caused errors, or to edit the metadata of a dataset, like the name of the labels when it needs to be changed.

## 5 Tasksource annotations

We select English datasets available on the HuggingFace Datasets Hub. We only consider discriminative tasks (Classification, Multiple-choice, Token Classification). We crawled all the tasks tagged with the English Language, and the Text-Classification task type<fnref n="6" /> or Multiple Choice tag⁷, as of January 2023.

As many tags are missing, to increase the coverage, we crawled the 1000 most popular datasets and used heuristics to identify discriminative tasks with labels with their fields names. We then ran a *fasttext* (Joulin et al., 2016) *langid* classifier to filter out untagged datasets with non-English text.

We only annotate datasets that do not require the user to manually download data or sign an agreement. We exclude datasets that require a particular library, with the exception of BIG-bench. We also exclude tasks where high accuracy is not desirable, such as bias probing tasks (Nangia et al., 2020) where accuracy measures bias, and tasks with input length that mostly exceeds 256 tokens.

We manually deduplicate the datasets which can be available individually or in benchmarks.

We also annotate the mapping between split names and train/validation/test splits. When the test splits are obfuscated (labels unavailable), we split the validation set and use half of it as a test set. Our goal is to reduce friction and individually submitting model test predictions to data owners

<footnote n="6">⁶https://hf.co/datasets?language=language:en&taskCategories=task Categories: text-classification&sort=downloads
⁷https://hf.co/datasets?task Categories=task Categories: multiple-choice&sort=downloads</footnote>
can take a lot of time. When no split is available, we do a 80/10/10% split. We use a fixed 0 random seed. to help reproducibility.

Label handling was one of the pain points of the testing of the preprocessing functions. The tasksource backend preprocesses text labels to map them to integers.

The Table in Appendix A enumerates all datasets annotated in the current version of tasksource<fnref n="8" />

# 6 Pretraining a model on tasksource

To demonstrate the potential of tasksource, we fine-tune a single deberta-base-v3 (He et al., 2021)⁹ text encoder on all tasksource tasks.

Following BERT (Devlin et al., 2019) standard setup, for token-classification tasks, we use a softmax classifier on top of the last layer encoded tokens to predict the token classes. For classification tasks and multiple-choice tasks, we use a classifier on top of the [CLS] sentinel token last layer.

We assign each task a different classification layer, but we tie the label weights (not biases) to each other if they are all identical.

We oversample datasets by a factor of 2 if they have less than 64k examples then cap dataset size to 64k examples to foster dataset diversity. We randomly sample a task for each batch with a frequency proportional to the capped training dataset size and we add a learnable task-specific sentinel token to the shared sentinel token. We drop the task-specific token 10% of the time to teach the model to also work without these task embeddings, to reduce mismatch when using our model with the vanilla DeBERTa architecture. We also noticed that this tended to improve general accuracy, since this forces cooperation across tasks.

We limit the number of choices to 4 for multiple-choice tasks, to limit redundant computations, as some datasets have more than 100 choices.

We use a learning rate of 3.10⁻⁵, a sequence length of 256, and a batch size of 24, with 16 accumulation steps to stabilize the multi-task optimization (Yu et al., 2020a). We did not perform hyperparameter optimization.

We used the `tasknet`¹⁰ library and a single

RTX-6000 24GB GPU for 7 days (20k steps). Using tasksource with `tasknet` enables concise multi-task training¹¹.

# 7 Results

As of January 2023, an early version of our model ranks first among 3574 base-sized¹² model on the *Model Recycling* (Choshen et al., 2022) external evaluation¹³ This evaluation comprises 36 representative English NLP tasks (Consisting of sentiment, NLI, Twitter, topic classification, and other general classification tasks), over 5 random seeds. These results are competitive with `deberta-large` models on GLUE. We did not observe any sign of overfitting yet which suggests that the network might still be undertrained.

# 8 Conclusion

We described a semantic, structured, concise, expressive dataset preprocessing annotation framework, which is associated with a parser and annotations, that can greatly facilitate new experiments for multi-task learning and improve reproducibility. We only scratched the surface of the potential of this generated task collection due to computational limitations. For future work, we plan to use tasksource to fully automate dataset parsing on new datasets with machine learning techniques to learn the parsing process. We also plan to work on a multilingual extension of tasksource annotations.

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A Currently annotated preprocessings

<table><thead><tr><th></th><th>preprocessing</th><th>task type</th></tr></thead><tbody><tr><td>0</td><td>glue/mnli (Williams et al., 2018)</td><td>Classification</td></tr><tr><td>1</td><td>glue/qnli (Williams et al., 2018)</td><td>Classification</td></tr><tr><td>2</td><td>glue/rte (Williams et al., 2018)</td><td>Classification</td></tr><tr><td>3</td><td>glue/wnli (Williams et al., 2018)</td><td>Classification</td></tr><tr><td>4</td><td>glue/mrpc (Williams et al., 2018)</td><td>Classification</td></tr><tr><td>5</td><td>glue/qqp (Williams et al., 2018)</td><td>Classification</td></tr><tr><td>6</td><td>glue/stsb (Williams et al., 2018)</td><td>Classification</td></tr><tr><td>7</td><td>super_glue/boolq (Clark et al., 2019)</td><td>Classification</td></tr><tr><td>8</td><td>super_glue/cb (De Marneffe et al., 2019)</td><td>Classification</td></tr><tr><td>9</td><td>super_glue/multirc (Khashabi et al., 2018)</td><td>Classification</td></tr><tr><td>10</td><td>super_glue/wic (Pilehvar and ose Camacho-Collados, 2018)</td><td>Classification</td></tr><tr><td>11</td><td>super_glue/axg (Rudinger et al., 2018)</td><td>Classification</td></tr><tr><td>12</td><td>anli/a1 (Nie et al., 2020)</td><td>Classification</td></tr><tr><td>13</td><td>anli/a2 (Nie et al., 2020)</td><td>Classification</td></tr><tr><td>14</td><td>anli/a3 (Nie et al., 2020)</td><td>Classification</td></tr><tr><td>15</td><td>babi_nli/lists-sets (Weston et al., 2015)</td><td>Classification</td></tr><tr><td>16</td><td>babi_nli/basic-deduction (Weston et al., 2015)</td><td>Classification</td></tr><tr><td>17</td><td>babi_nli/positional-reasoning (Weston et al., 2015)</td><td>Classification</td></tr><tr><td>18</td><td>babi_nli/basic-coreference (Weston et al., 2015)</td><td>Classification</td></tr><tr><td>19</td><td>babi_nli/three-supporting-facts (Weston et al., 2015)</td><td>Classification</td></tr><tr><td>20</td><td>babi_nli/path-finding (Weston et al., 2015)</td><td>Classification</td></tr><tr><td>21</td><td>babi_nli/three-arg-relations (Weston et al., 2015)</td><td>Classification</td></tr><tr><td>22</td><td>babi_nli/yes-no-questions (Weston et al., 2015)</td><td>Classification</td></tr><tr><td>23</td><td>babi_nli/time-reasoning (Weston et al., 2015)</td><td>Classification</td></tr><tr><td>24</td><td>babi_nli/indefinite-knowledge (Weston et al., 2015)</td><td>Classification</td></tr><tr><td>25</td><td>babi_nli/counting (Weston et al., 2015)</td><td>Classification</td></tr><tr><td>26</td><td>babi_nli/size-reasoning (Weston et al., 2015)</td><td>Classification</td></tr><tr><td>27</td><td>babi_nli/compound-coreference (Weston et al., 2015)</td><td>Classification</td></tr><tr><td>28</td><td>babi_nli/basic-induction (Weston et al., 2015)</td><td>Classification</td></tr><tr><td>29</td><td>babi_nli/single-supporting-fact (Weston et al., 2015)</td><td>Classification</td></tr><tr><td>30</td><td>babi_nli/simple-negation (Weston et al., 2015)</td><td>Classification</td></tr><tr><td>31</td><td>babi_nli/two-arg-relations (Weston et al., 2015)</td><td>Classification</td></tr><tr><td>32</td><td>babi_nli/two-supporting-facts (Weston et al., 2015)</td><td>Classification</td></tr><tr><td>33</td><td>babi_nli/conjunction (Weston et al., 2015)</td><td>Classification</td></tr><tr><td>34</td><td>sick/label (Marelli et al., 2014)</td><td>Classification</td></tr><tr><td>35</td><td>sick/relatedness (Marelli et al., 2014)</td><td>Classification</td></tr><tr><td>36</td><td>sick/entailment_AB (Marelli et al., 2014)</td><td>Classification</td></tr><tr><td>37</td><td>snli (Bowman et al., 2015)</td><td>Classification</td></tr><tr><td>38</td><td>scitail/snli_format (Khot et al., 2018)</td><td>Classification</td></tr><tr><td>39</td><td>hans (McCoy et al., 2019)</td><td>Classification</td></tr><tr><td>40</td><td>WANLI (Liu et al., 2022)</td><td>Classification</td></tr><tr><td>41</td><td>recast/recast_verbcorner (Poliak et al., 2018)</td><td>Classification</td></tr><tr><td>42</td><td>recast/recast_megaveridicality (Poliak et al., 2018)</td><td>Classification</td></tr><tr><td>43</td><td>recast/recast_sentiment (Poliak et al., 2018)</td><td>Classification</td></tr><tr><td>44</td><td>recast/recast_ner (Poliak et al., 2018)</td><td>Classification</td></tr><tr><td>45</td><td>recast/recast_kg_relations (Poliak et al., 2018)</td><td>Classification</td></tr></tbody></table>

Continued on next page
<table><thead><tr><th></th><th>preprocessing</th><th>task type</th></tr></thead><tbody><tr><td>46</td><td>recast/recast_factuality (Poliak et al., 2018)</td><td>Classification</td></tr><tr><td>47</td><td>recast/recast_puns (Poliak et al., 2018)</td><td>Classification</td></tr><tr><td>48</td><td>recast/recast_verbnet (Poliak et al., 2018)</td><td>Classification</td></tr><tr><td>49</td><td>probability_words_nli/reasoning_1hop (Sileo and Moens, 2022b)</td><td>Classification</td></tr><tr><td>50</td><td>probability_words_nli/usnli (Sileo and Moens, 2022b)</td><td>Classification</td></tr><tr><td>51</td><td>probability_words_nli/reasoning_2hop (Sileo and Moens, 2022b)</td><td>Classification</td></tr><tr><td>52</td><td>nan-nli/joey234-nan-nli</td><td>Classification</td></tr><tr><td>53</td><td>nli_fever</td><td>Classification</td></tr><tr><td>54</td><td>breaking_nli</td><td>Classification</td></tr><tr><td>55</td><td>conj_nli</td><td>Classification</td></tr><tr><td>56</td><td>fracas</td><td>Classification</td></tr><tr><td>57</td><td>dialogue_nli</td><td>Classification</td></tr><tr><td>58</td><td>mpe</td><td>Classification</td></tr><tr><td>59</td><td>dnc</td><td>Classification</td></tr><tr><td>60</td><td>recast_white/fnplus</td><td>Classification</td></tr><tr><td>61</td><td>recast_white/sprl</td><td>Classification</td></tr><tr><td>62</td><td>recast_white/dpr</td><td>Classification</td></tr><tr><td>63</td><td>joci</td><td>Classification</td></tr><tr><td>64</td><td>robust_nli/IS_CS</td><td>Classification</td></tr><tr><td>65</td><td>robust_nli/LI_LI</td><td>Classification</td></tr><tr><td>66</td><td>robust_nli/ST_WO</td><td>Classification</td></tr><tr><td>67</td><td>robust_nli/PI_SP</td><td>Classification</td></tr><tr><td>68</td><td>robust_nli/PI_CD</td><td>Classification</td></tr><tr><td>69</td><td>robust_nli/ST_SE</td><td>Classification</td></tr><tr><td>70</td><td>robust_nli/ST_NE</td><td>Classification</td></tr><tr><td>71</td><td>robust_nli/ST_LM</td><td>Classification</td></tr><tr><td>72</td><td>robust_nli_is_sd</td><td>Classification</td></tr><tr><td>73</td><td>robust_nli_li_ts</td><td>Classification</td></tr><tr><td>74</td><td>gen_debiased_nli/snli_seq_z</td><td>Classification</td></tr><tr><td>75</td><td>gen_debiased_nli/snli_z_aug</td><td>Classification</td></tr><tr><td>76</td><td>gen_debiased_nli/snli_par_z</td><td>Classification</td></tr><tr><td>77</td><td>gen_debiased_nli/mnli_par_z</td><td>Classification</td></tr><tr><td>78</td><td>gen_debiased_nli/mnli_z_aug</td><td>Classification</td></tr><tr><td>79</td><td>gen_debiased_nli/mnli_seq_z</td><td>Classification</td></tr><tr><td>80</td><td>add_one_rte</td><td>Classification</td></tr><tr><td>81</td><td>imppres/presupposition_all_n_presupposition (Jereti c et al., 2020)</td><td>Classification</td></tr><tr><td>82</td><td>imppres/presuppositionPossessed_definites_existence (Jereti c et al., 2020)</td><td>Classification</td></tr><tr><td>83</td><td>imppres/presupposition_cleft_uniqueness(Jereti c et al., 2020)</td><td>Classification</td></tr><tr><td>84</td><td>imppres/presupposition question_presupposition(Jereti c et al., 2020)</td><td>Classification</td></tr><tr><td>85</td><td>imppres/presuppositionPossessed_definites_uniqueness(Jereti c et al., 2020)</td><td>Classification</td></tr><tr><td>86</td><td>imppres/presupposition_only_presupposition(Jereti c et al., 2020)</td><td>Classification</td></tr><tr><td>87</td><td>imppres/presupposition both_presupposition(Jereti c et al., 2020)</td><td>Classification</td></tr><tr><td>88</td><td>imppres/presupposition_change_of_state(Jereti c et al., 2020)</td><td>Classification</td></tr><tr><td>89</td><td>imppres/presupposition_cleft_existence(Jereti c et al., 2020)</td><td>Classification</td></tr><tr><td>90</td><td>imppres/implicatureader_qantifiers/prag (Jereti c et al., 2020)</td><td>Classification</td></tr><tr><td>91</td><td>imppres/implicature_numerals_2_3/prag (Jereti c et al., 2020)</td><td>Classification</td></tr><tr><td>92</td><td>imppres/implicature_numerals_10_100/prag (Jereti c et al., 2020)</td><td>Classification</td></tr><tr><td>93</td><td>imppres/implicature_modals/prag (Jereti c et al., 2020)</td><td>Classification</td></tr></tbody><tfoot><tr><td colspan="3">Continued on next page</td></tr></tfoot></table>
<table><thead><tr><th></th><th>preprocessing</th><th>task type</th></tr></thead><tbody><tr><td>94</td><td>imppres/implicature_connectives/prag (Jereti c et al., 2020)</td><td>Classification</td></tr><tr><td>95</td><td>imppres/implicatureGradable_verb/prag (Jereti c et al., 2020)</td><td>Classification</td></tr><tr><td>96</td><td>imppres/implicatureGradable_adjective/prag (Jereti c et al., 2020)</td><td>Classification</td></tr><tr><td>97</td><td>imppres/implicatureQuantifiers/log (Jereti c et al., 2020)</td><td>Classification</td></tr><tr><td>98</td><td>imppres/implicatureNumerals_2_3/log (Jereti c et al., 2020)</td><td>Classification</td></tr><tr><td>99</td><td>imppres/implicatureNumerals_10_100/log (Jereti c et al., 2020)</td><td>Classification</td></tr><tr><td>100</td><td>imppres/implicatureGradable_adjective/log (Jereti c et al., 2020)</td><td>Classification</td></tr><tr><td>101</td><td>imppres/implicatureConnectives/log (Jereti c et al., 2020)</td><td>Classification</td></tr><tr><td>102</td><td>imppres/implicatureModals/log (Jereti c et al., 2020)</td><td>Classification</td></tr><tr><td>103</td><td>imppres/implicatureGradable_verb/log (Jereti c et al., 2020)</td><td>Classification</td></tr><tr><td>104</td><td>glue_diagnostics/diagnostics</td><td>Classification</td></tr><tr><td>105</td><td>hlgd (Laban and Bandarkar, 2021)</td><td>Classification</td></tr><tr><td>106</td><td>paws/labeled_final (Zhang et al., 2019)</td><td>Classification</td></tr><tr><td>107</td><td>paws/labeled_swap (Zhang et al., 2019)</td><td>Classification</td></tr><tr><td>108</td><td>quora</td><td>Classification</td></tr><tr><td>109</td><td>medical_questions.pairs (McCreery et al., 2020)</td><td>Classification</td></tr><tr><td>110</td><td>glue/cola (Williams et al., 2018)</td><td>Classification</td></tr><tr><td>111</td><td>glue/sst2 (Williams et al., 2018)</td><td>Classification</td></tr><tr><td>112</td><td>utilitarianism (Hendrycks et al., 2020)</td><td>Classification</td></tr><tr><td>113</td><td>amazon_counterfactual/en (ONeill et al., 2021)</td><td>Classification</td></tr><tr><td>114</td><td>insincere-questions</td><td>Classification</td></tr><tr><td>115</td><td>toxic_conversations</td><td>Classification</td></tr><tr><td>116</td><td>TuringBench (huggingface, 2020)</td><td>Classification</td></tr><tr><td>117</td><td>trec (Li and Roth, 2002)</td><td>Classification</td></tr><tr><td>118</td><td>vitaminc/tals-vitaminc (Schuster et al., 2021)</td><td>Classification</td></tr><tr><td>119</td><td>hope_edi/english (Chakravarthi, 2020)</td><td>Classification</td></tr><tr><td>120</td><td>rumoueval_2019/RumourEval2019 (Gorrell et al., 2019)</td><td>Classification</td></tr><tr><td>121</td><td>ethos/binary (Mollas et al., 2020)</td><td>Classification</td></tr><tr><td>122</td><td>ethos/multilabel (Mollas et al., 2020)</td><td>Classification</td></tr><tr><td>123</td><td>tweet_eval/emotion (Barbieri et al., 2020)</td><td>Classification</td></tr><tr><td>124</td><td>tweet_eval/irony (Barbieri et al., 2020)</td><td>Classification</td></tr><tr><td>125</td><td>tweet_eval/offensive (Barbieri et al., 2020)</td><td>Classification</td></tr><tr><td>126</td><td>tweet_eval/sentiment (Barbieri et al., 2020)</td><td>Classification</td></tr><tr><td>127</td><td>tweet_eval/stance_abortion (Barbieri et al., 2020)</td><td>Classification</td></tr><tr><td>128</td><td>tweet_eval/stance_atheism (Barbieri et al., 2020)</td><td>Classification</td></tr><tr><td>129</td><td>tweet_eval/stance_climate (Barbieri et al., 2020)</td><td>Classification</td></tr><tr><td>130</td><td>tweet_eval/stance_feminist (Barbieri et al., 2020)</td><td>Classification</td></tr><tr><td>131</td><td>tweet_eval/stance_hillary (Barbieri et al., 2020)</td><td>Classification</td></tr><tr><td>132</td><td>tweet_eval/emoji (Barbieri et al., 2020)</td><td>Classification</td></tr><tr><td>133</td><td>tweet_eval/hate (Barbieri et al., 2020)</td><td>Classification</td></tr><tr><td>134</td><td>discovery/discovery (Sileo et al., 2019)</td><td>Classification</td></tr><tr><td>135</td><td>pragmeval/squinky-informativeness (Lahiri, 2015)</td><td>Classification</td></tr><tr><td>136</td><td>pragmeval/squinky-implicature (Lahiri, 2015)</td><td>Classification</td></tr><tr><td>137</td><td>pragmeval/verifiability (Park and Cardie, 2014)</td><td>Classification</td></tr><tr><td>138</td><td>pragmeval/squinky-formality (Lahiri, 2015)</td><td>Classification</td></tr><tr><td>139</td><td>pragmeval/emobank-valence (Buechel and Hahn, 2017)</td><td>Classification</td></tr><tr><td>140</td><td>pragmeval/emobank-dominance (Buechel and Hahn, 2017)</td><td>Classification</td></tr><tr><td>141</td><td>pragmeval/emobank-arousal (Buechel and Hahn, 2017)</td><td>Classification</td></tr></tbody><tfoot><tr><td colspan="3">Continued on next page</td></tr></tfoot></table>
<table><thead><tr><th></th><th>preprocessing</th><th>task type</th></tr></thead><tbody><tr><td>142</td><td>pragmeval/switchboard (Godfrey et al., 1992)</td><td>Classification</td></tr><tr><td>143</td><td>pragmeval/mrda (Shriberg et al., 2004)</td><td>Classification</td></tr><tr><td>144</td><td>pragmeval/sarcasm (Oraby et al., 2016)</td><td>Classification</td></tr><tr><td>145</td><td>pragmeval/persuasiveness-premisetype (Carlile et al., 2018)</td><td>Classification</td></tr><tr><td>146</td><td>pragmeval/persuasiveness-eloquence (Carlile et al., 2018)</td><td>Classification</td></tr><tr><td>147</td><td>pragmeval/persuasiveness-claimtype (Carlile et al., 2018)</td><td>Classification</td></tr><tr><td>148</td><td>pragmeval/persuasiveness-specificity (Carlile et al., 2018)</td><td>Classification</td></tr><tr><td>149</td><td>pragmeval/gum (Zeldes, 2017)</td><td>Classification</td></tr><tr><td>150</td><td>pragmeval/emergent (Ferreira and Vlachos, 2016)</td><td>Classification</td></tr><tr><td>151</td><td>pragmeval/persuasiveness-strength (Carlile et al., 2018)</td><td>Classification</td></tr><tr><td>152</td><td>pragmeval/stac (Asher et al., 2016)</td><td>Classification</td></tr><tr><td>153</td><td>pragmeval/pdtb (Prasad et al., 2008)</td><td>Classification</td></tr><tr><td>154</td><td>pragmeval/persuasiveness-relevance (Carlile et al., 2018)</td><td>Classification</td></tr><tr><td>155</td><td>silicone/meld_s (Chen et al., 2018)</td><td>Classification</td></tr><tr><td>156</td><td>silicone/sem (McKeown et al., 2011)</td><td>Classification</td></tr><tr><td>157</td><td>silicone/oasis (Leech and Weisser, 2003)</td><td>Classification</td></tr><tr><td>158</td><td>silicone/meld_e (Chen et al., 2018)</td><td>Classification</td></tr><tr><td>159</td><td>silicone/maptask (Thompson et al., 1993)</td><td>Classification</td></tr><tr><td>160</td><td>silicone/iemocap (Busso et al., 2008)</td><td>Classification</td></tr><tr><td>161</td><td>silicone/dyda_e (Li et al., 2017)</td><td>Classification</td></tr><tr><td>162</td><td>silicone/dyda_da (Li et al., 2017)</td><td>Classification</td></tr><tr><td>163</td><td>lex_glue/eurlex (Chalkidis et al., 2021)</td><td>Classification</td></tr><tr><td>164</td><td>lex_glue/scotus (Spaeth et al., 2020)</td><td>Classification</td></tr><tr><td>165</td><td>lex_glue/ledgar (Tuggener et al., 2020)</td><td>Classification</td></tr><tr><td>166</td><td>lex_glue/unfair_tos (Lippi et al., 2019)</td><td>Classification</td></tr><tr><td>167</td><td>language-identification</td><td>Classification</td></tr><tr><td>168</td><td>imdb (Maas et al., 2011)</td><td>Classification</td></tr><tr><td>169</td><td>rotten_tomatoes (Pang and Lee, 2005)</td><td>Classification</td></tr><tr><td>170</td><td>ag_news (Zhang et al., 2015b)</td><td>Classification</td></tr><tr><td>171</td><td>yelp_review_full/yelp_review_full (Zhang et al., 2015a)</td><td>Classification</td></tr><tr><td>172</td><td>financialPhrasebank/sentences_allagree (Malo et al., 2014)</td><td>Classification</td></tr><tr><td>173</td><td>poem_sentiment (Sheng and Uthus, 2020)</td><td>Classification</td></tr><tr><td>174</td><td>dbpedia_14/dbpedia_14 (Lehmann et al., 2015)</td><td>Classification</td></tr><tr><td>175</td><td>amazon_polarity/amazon_polarity (McAuley and Leskovec, 2013)</td><td>Classification</td></tr><tr><td>176</td><td>appreviews (Zur, 2017)</td><td>Classification</td></tr><tr><td>177</td><td>hate_speech18 (de Gibert et al., 2018)</td><td>Classification</td></tr><tr><td>178</td><td>sms_spam (Almeida et al., 2011)</td><td>Classification</td></tr><tr><td>179</td><td>humicroedit/subtask-1 (Hossain et al., 2019)</td><td>Classification</td></tr><tr><td>180</td><td>humicroedit/subtask-2 (Hossain et al., 2019)</td><td>Classification</td></tr><tr><td>181</td><td>snips_built_in_intents (Coucke et al., 2018)</td><td>Classification</td></tr><tr><td>182</td><td>banking77 (Casanueva et al., 2020)</td><td>Classification</td></tr><tr><td>183</td><td>hate_speech_offensive (Davidson et al., 2017)</td><td>Classification</td></tr><tr><td>184</td><td>yahoo topics</td><td>Classification</td></tr><tr><td>185</td><td> stackoverflow-questions</td><td>Classification</td></tr><tr><td>186</td><td>hyperpartisan_news</td><td>Classification</td></tr><tr><td>187</td><td>sciie</td><td>Classification</td></tr><tr><td>188</td><td>citation intent</td><td>Classification</td></tr><tr><td>189</td><td>go_emotions/simplified (Demszky et al., 2020)</td><td>Classification</td></tr></tbody><tfoot><tr><td colspan="3">Continued on next page</td></tr></tfoot></table>
<table><thead><tr><th></th><th>preprocessing</th><th>task type</th></tr></thead><tbody><tr><td>190</td><td>scicite (Cohan et al., 2019)</td><td>Classification</td></tr><tr><td>191</td><td>liar (Wang, 2017)</td><td>Classification</td></tr><tr><td>192</td><td>lexical_relation_classification/K&amp;H+N (Wang et al., 2019b)</td><td>Classification</td></tr><tr><td>193</td><td>lexical_relation_classification/CogALexV (Wang et al., 2019b)</td><td>Classification</td></tr><tr><td>194</td><td>lexical_relation_classification/BLESS (Wang et al., 2019b)</td><td>Classification</td></tr><tr><td>195</td><td>lexical_relation_classification/EVALution (Wang et al., 2019b)</td><td>Classification</td></tr><tr><td>196</td><td>lexical_relation_classification/ROOT09 (Wang et al., 2019b)</td><td>Classification</td></tr><tr><td>197</td><td>linguisticprobing/subj_number (Conneau et al., 2018)</td><td>Classification</td></tr><tr><td>198</td><td>linguisticprobing/bigram_shift (Conneau et al., 2018)</td><td>Classification</td></tr><tr><td>199</td><td>linguisticprobing/top_constituents (Conneau et al., 2018)</td><td>Classification</td></tr><tr><td>200</td><td>linguisticprobing/odd_man_out (Conneau et al., 2018)</td><td>Classification</td></tr><tr><td>201</td><td>linguisticprobing/past_present (Conneau et al., 2018)</td><td>Classification</td></tr><tr><td>202</td><td>linguisticprobing/coordination_inversion (Conneau et al., 2018)</td><td>Classification</td></tr><tr><td>203</td><td>linguisticprobing/tree_depth (Conneau et al., 2018)</td><td>Classification</td></tr><tr><td>204</td><td>linguisticprobing/obj_number (Conneau et al., 2018)</td><td>Classification</td></tr><tr><td>205</td><td>linguisticprobing/sentence_length (Conneau et al., 2018)</td><td>Classification</td></tr><tr><td>206</td><td>crowdflower/sentiment_nuclear_power (Van Pelt and Sorokin, 2012)</td><td>Classification</td></tr><tr><td>207</td><td>crowdflower/tweet_global_warming (Van Pelt and Sorokin, 2012)</td><td>Classification</td></tr><tr><td>208</td><td>crowdflower/corporate-messaging (Van Pelt and Sorokin, 2012)</td><td>Classification</td></tr><tr><td>209</td><td>crowdflower/economic-news (Van Pelt and Sorokin, 2012)</td><td>Classification</td></tr><tr><td>210</td><td>crowdflower/airline-sentiment (Van Pelt and Sorokin, 2012)</td><td>Classification</td></tr><tr><td>211</td><td>crowdflower/political-media-bias (Van Pelt and Sorokin, 2012)</td><td>Classification</td></tr><tr><td>212</td><td>crowdflower/text_emotion (Van Pelt and Sorokin, 2012)</td><td>Classification</td></tr><tr><td>213</td><td>crowdflower/political-media-audience (Van Pelt and Sorokin, 2012)</td><td>Classification</td></tr><tr><td>214</td><td>crowdflower/political-media-message (Van Pelt and Sorokin, 2012)</td><td>Classification</td></tr><tr><td>215</td><td>ethics/commonsense (Hendrycks et al., 2020)</td><td>Classification</td></tr><tr><td>216</td><td>ethics/deontology (Hendrycks et al., 2020)</td><td>Classification</td></tr><tr><td>217</td><td>ethics/justice (Hendrycks et al., 2020)</td><td>Classification</td></tr><tr><td>218</td><td>ethics/virtue (Hendrycks et al., 2020)</td><td>Classification</td></tr><tr><td>219</td><td>emo/emo2019 (Chatterjee et al., 2019)</td><td>Classification</td></tr><tr><td>220</td><td>google_wellformed_query (Faruqui and Das, 2018)</td><td>Classification</td></tr><tr><td>221</td><td>tweets_hate_speech_detection (ZR0, 2018)</td><td>Classification</td></tr><tr><td>222</td><td>has_part (Bhakthavatsalam et al., 2020)</td><td>Classification</td></tr><tr><td>223</td><td>blog_authorship_corpus/gender (Schler et al., 2006)</td><td>Classification</td></tr><tr><td>224</td><td>blog_authorship_corpus/age (Schler et al., 2006)</td><td>Classification</td></tr><tr><td>225</td><td>blog_authorship_corpus/horoscope (Schler et al., 2006)</td><td>Classification</td></tr><tr><td>226</td><td>blog_authorship_corpus/job (Schler et al., 2006)</td><td>Classification</td></tr><tr><td>227</td><td>open question_type (Cao and Wang, 2021)</td><td>Classification</td></tr><tr><td>228</td><td>health (Kotonya and Toni, 2020)</td><td>Classification</td></tr><tr><td>229</td><td>mc_taco (Ben Zhou and Roth, 2019)</td><td>Classification</td></tr><tr><td>230</td><td>ade_corpus_v2/Ade_corpus_v2_classification (Gurulingappa et al., 2012)</td><td>Classification</td></tr><tr><td>231</td><td>circa (Louis et al., 2020)</td><td>Classification</td></tr><tr><td>232</td><td>EffectiveFeedbackStudentWriting</td><td>Classification</td></tr><tr><td>233</td><td>promptSentiment (McAuley and Leskovec, 2013)</td><td>Classification</td></tr><tr><td>234</td><td>promptNLI (Nie et al., 2020)</td><td>Classification</td></tr><tr><td>235</td><td>promptSpoke</td><td>Classification</td></tr><tr><td>236</td><td>promptProficiency</td><td>Classification</td></tr><tr><td>237</td><td>promptGrammar (Warstadt et al., 2018)</td><td>Classification</td></tr></tbody><tfoot><tr><td colspan="3">Continued on next page</td></tr></tfoot></table>
<table><thead><tr><th></th><th>preprocessing</th><th>task type</th></tr></thead><tbody><tr><td>238</td><td>promptCoherence</td><td>Classification</td></tr><tr><td>239</td><td>phrase_similarity (Pham et al., 2022)</td><td>Classification</td></tr><tr><td>240</td><td>scientific-exaggeration-detection (Wright and Augenstein, 2021)</td><td>Classification</td></tr><tr><td>241</td><td>quarel</td><td>Classification</td></tr><tr><td>242</td><td>fever-evidence-related/mwong-fever-related</td><td>Classification</td></tr><tr><td>243</td><td>numer_sense (Lin et al., 2020)</td><td>Classification</td></tr><tr><td>244</td><td>dynasent/dynabench.dynasent.r1.all/r1 (Potts et al., 2020)</td><td>Classification</td></tr><tr><td>245</td><td>dynasent/dynabench.dynasent.r2.all/r2 (Potts et al., 2020)</td><td>Classification</td></tr><tr><td>246</td><td>Sarcasm_News_Headline</td><td>Classification</td></tr><tr><td>247</td><td>sem_eval_2010_task_8 (Hendrickx et al., 2010)</td><td>Classification</td></tr><tr><td>248</td><td>auditor_review/demo-org-auditor_review</td><td>Classification</td></tr><tr><td>249</td><td>Dynasent_Disagreement</td><td>Classification</td></tr><tr><td>250</td><td>Politeness_Disagreement</td><td>Classification</td></tr><tr><td>251</td><td>SBIC_Disagreement</td><td>Classification</td></tr><tr><td>252</td><td>SChem_Disagreement</td><td>Classification</td></tr><tr><td>253</td><td>Dilemmas_Disagreement</td><td>Classification</td></tr><tr><td>254</td><td>wiki_qa (Y et al., 2015)</td><td>Classification</td></tr><tr><td>255</td><td>cyclic_classification (Kejriwal and Shen, 2020)</td><td>Classification</td></tr><tr><td>256</td><td>sts-companion (Cer et al., 2017)</td><td>Classification</td></tr><tr><td>257</td><td>commonsense_qa_2.0</td><td>Classification</td></tr><tr><td>258</td><td>lingnli (Parrish et al., 2021)</td><td>Classification</td></tr><tr><td>259</td><td>monotonicity-entailment (Yanaka et al., 2019a)</td><td>Classification</td></tr><tr><td>260</td><td>scinli (Sadat and Caragea, 2022)</td><td>Classification</td></tr><tr><td>261</td><td>naturallogic (Feng et al., 2020)</td><td>Classification</td></tr><tr><td>262</td><td>dynahate (Vidgen et al., 2021)</td><td>Classification</td></tr><tr><td>263</td><td>syntactic-augmentation-nli (Min et al., 2020)</td><td>Classification</td></tr><tr><td>264</td><td>autotnli</td><td>Classification</td></tr><tr><td>265</td><td>CONDAQA (Ravichander et al., 2022)</td><td>Classification</td></tr><tr><td>266</td><td>scruples</td><td>Classification</td></tr><tr><td>267</td><td>attempto-nli</td><td>Classification</td></tr><tr><td>268</td><td>defeasible-nli/atomic</td><td>Classification</td></tr><tr><td>269</td><td>defeasible-nli/snli</td><td>Classification</td></tr><tr><td>270</td><td>help-nli (Yanaka et al., 2019b)</td><td>Classification</td></tr><tr><td>271</td><td>nli-veridicality-transitivity (Yanaka et al., 2021)</td><td>Classification</td></tr><tr><td>272</td><td>natural-language-satisfiability (Schlegel et al., 2022)</td><td>Classification</td></tr><tr><td>273</td><td>lonli (Tarunesh et al., 2021)</td><td>Classification</td></tr><tr><td>274</td><td>dadc-limit-nli (Wallace et al., 2022)</td><td>Classification</td></tr><tr><td>275</td><td>FLUTE</td><td>Classification</td></tr><tr><td>276</td><td>strategy-qa</td><td>Classification</td></tr><tr><td>277</td><td>folio (Han et al., 2022)</td><td>Classification</td></tr><tr><td>278</td><td>tomli-nli</td><td>Classification</td></tr><tr><td>279</td><td>avicenna (Aghahadi and Talebpour, 2022)</td><td>Classification</td></tr><tr><td>280</td><td>CREAK</td><td>Classification</td></tr><tr><td>281</td><td>puzzte (Szomiu and Groza, 2021)</td><td>Classification</td></tr><tr><td>282</td><td>spartqa-yn (Mirzaee et al., 2021)</td><td>Classification</td></tr><tr><td>283</td><td>temporal-nli (Thukral et al., 2021)</td><td>Classification</td></tr><tr><td>284</td><td>clcd-english</td><td>Classification</td></tr><tr><td>285</td><td>twentyquestions</td><td>Classification</td></tr></tbody><tfoot><tr><td colspan="3">Continued on next page</td></tr></tfoot></table>
<table><thead><tr><th></th><th>preprocessing</th><th>task type</th></tr></thead><tbody><tr><td>286</td><td>counterfactually-augmented-imdb (Kaushik et al., 2020)</td><td>Classification</td></tr><tr><td>287</td><td>counterfactually-augmented-snli (Kaushik et al., 2020)</td><td>Classification</td></tr><tr><td>288</td><td>cnli (Huang et al., 2020)</td><td>Classification</td></tr><tr><td>289</td><td>boolq-natural-perturbations (Khashabi et al., 2020a)</td><td>Classification</td></tr><tr><td>290</td><td>acceptability-prediction (Lau et al., 2015)</td><td>Classification</td></tr><tr><td>291</td><td>equate (Ravichander et al., 2019)</td><td>Classification</td></tr><tr><td>292</td><td>implicit-hate-stg1 (ElSherief et al., 2021)</td><td>Classification</td></tr><tr><td>293</td><td>chaos-mnli-ambiguity (Zhou et al., 2022)</td><td>Classification</td></tr><tr><td>294</td><td>headline_cause/en_simple (Gusev and Tikhonov, 2021)</td><td>Classification</td></tr><tr><td>295</td><td>logiqa-2.0-nli</td><td>Classification</td></tr><tr><td>296</td><td>oasst1_dense_flat/quality</td><td>Classification</td></tr><tr><td>297</td><td>oasst1_dense_flat/toxicity</td><td>Classification</td></tr><tr><td>298</td><td>oasst1_dense_flat/helpfulness</td><td>Classification</td></tr><tr><td>299</td><td>PARARULE-Plus (Bao et al., 2022)</td><td>Classification</td></tr><tr><td>300</td><td>mindgames (Sileo and Lernould, 2023)</td><td>Classification</td></tr><tr><td>301</td><td>ambient (Liu et al., 2023)</td><td>Classification</td></tr><tr><td>302</td><td>civil_commments/toxicity (Borkan et al., 2019)</td><td>Classification</td></tr><tr><td>303</td><td>civil_commments/severe_toxicity (Borkan et al., 2019)</td><td>Classification</td></tr><tr><td>304</td><td>civil_commments/obscene (Borkan et al., 2019)</td><td>Classification</td></tr><tr><td>305</td><td>civil_commments/threat (Borkan et al., 2019)</td><td>Classification</td></tr><tr><td>306</td><td>civil_commments/insult (Borkan et al., 2019)</td><td>Classification</td></tr><tr><td>307</td><td>civil_commments/identityattack (Borkan et al., 2019)</td><td>Classification</td></tr><tr><td>308</td><td>civil_commments/sexual_explicit (Borkan et al., 2019)</td><td>Classification</td></tr><tr><td>309</td><td>I2D2</td><td>Classification</td></tr><tr><td>310</td><td>hh-rlhf</td><td>MultipleChoice</td></tr><tr><td>311</td><td>model-written-evals (Perez et al., 2022)</td><td>MultipleChoice</td></tr><tr><td>312</td><td>truthful_qa/multipleChoice (Lin et al., 2021b)</td><td>MultipleChoice</td></tr><tr><td>313</td><td>fig-qa</td><td>MultipleChoice</td></tr><tr><td>314</td><td>bigbench/strange_stories (Srivastava et al., 2022b)</td><td>MultipleChoice</td></tr><tr><td>315</td><td>bigbench/arithmetic (Srivastava et al., 2022b)</td><td>MultipleChoice</td></tr><tr><td>316</td><td>bigbench/formal_fallacies_syllogisms_negation (Srivastava et al., 2022b)</td><td>MultipleChoice</td></tr><tr><td>317</td><td>bigbench/implicatures (Srivastava et al., 2022b)</td><td>MultipleChoice</td></tr><tr><td>318</td><td>bigbench/salient_translation_error_detection (Srivastava et al., 2022b)</td><td>MultipleChoice</td></tr><tr><td>319</td><td>bigbench/causal_judgment (Srivastava et al., 2022b)</td><td>MultipleChoice</td></tr><tr><td>320</td><td>bigbench/discourse_markersprediction (Srivastava et al., 2022b)</td><td>MultipleChoice</td></tr><tr><td>321</td><td>bigbench/timedial (Srivastava et al., 2022b)</td><td>MultipleChoice</td></tr><tr><td>322</td><td>bigbench/general_knowledge (Srivastava et al., 2022b)</td><td>MultipleChoice</td></tr><tr><td>323</td><td>bigbench/evaluating_information_essentiality (Srivastava et al., 2022b)</td><td>MultipleChoice</td></tr><tr><td>324</td><td>bigbench/cause_and_effect (Srivastava et al., 2022b)</td><td>MultipleChoice</td></tr><tr><td>325</td><td>bigbench/hyperbaton (Srivastava et al., 2022b)</td><td>MultipleChoice</td></tr><tr><td>326</td><td>bigbench/hindu_knowledge (Srivastava et al., 2022b)</td><td>MultipleChoice</td></tr><tr><td>327</td><td>bigbench/crass_ai (Srivastava et al., 2022b)</td><td>MultipleChoice</td></tr><tr><td>328</td><td>bigbench/movie_recommendation (Srivastava et al., 2022b)</td><td>MultipleChoice</td></tr><tr><td>329</td><td>bigbench/cifar10_classification (Srivastava et al., 2022b)</td><td>MultipleChoice</td></tr><tr><td>330</td><td>bigbench/logic_grid_puzzle (Srivastava et al., 2022b)</td><td>MultipleChoice</td></tr><tr><td>331</td><td>bigbench/sentence_ambiguity (Srivastava et al., 2022b)</td><td>MultipleChoice</td></tr><tr><td>332</td><td>bigbench/factChecker (Srivastava et al., 2022b)</td><td>MultipleChoice</td></tr><tr><td>333</td><td>bigbench/strategyqa (Srivastava et al., 2022b)</td><td>MultipleChoice</td></tr></tbody><tfoot><tr><td colspan="3">Continued on next page</td></tr></tfoot></table>
<table><thead><tr><th></th><th>preprocessing</th><th>task type</th></tr></thead><tbody><tr><td>334</td><td>bigbench/elementary</td><td>MultipleChoice</td></tr><tr><td>335</td><td>bigbench/temporal_sequences</td><td>MultipleChoice</td></tr><tr><td>336</td><td>bigbench/penguins_in_a_table</td><td>MultipleChoice</td></tr><tr><td>337</td><td>bigbench/goal_step_wikihow</td><td>MultipleChoice</td></tr><tr><td>338</td><td>bigbench/dark_humor_detection</td><td>MultipleChoice</td></tr><tr><td>339</td><td>bigbench/logical_fallacy_detection</td><td>MultipleChoice</td></tr><tr><td>340</td><td>bigbench/irony_identification</td><td>MultipleChoice</td></tr><tr><td>341</td><td>bigbench/emojis_emotionente</td><td>MultipleChoice</td></tr><tr><td>342</td><td>bigbench/sports_understanding</td><td>MultipleChoice</td></tr><tr><td>343</td><td>bigbench/contextual_parametric_knowledge_conflicts</td><td>MultipleChoice</td></tr><tr><td>344</td><td>bigbench/intent_recognition</td><td>MultipleChoice</td></tr><tr><td>345</td><td>bigbench/crash_blossom</td><td>MultipleChoice</td></tr><tr><td>346</td><td>bigbench/real_or_fake_text</td><td>MultipleChoice</td></tr><tr><td>347</td><td>bigbench/ruin_names</td><td>MultipleChoice</td></tr><tr><td>348</td><td>bigbench/logical_deduction</td><td>MultipleChoice</td></tr><tr><td>349</td><td>bigbench/identify</td><td>MultipleChoice</td></tr><tr><td>350</td><td>bigbench/vitaminc_fact_verify</td><td>MultipleChoice</td></tr><tr><td>351</td><td>bigbench/hhhonic</td><td>MultipleChoice</td></tr><tr><td>352</td><td>bigbench/simple_ethical_questions</td><td>MultipleChoice</td></tr><tr><td>353</td><td>bigbench/checkmate_in_one</td><td>MultipleChoice</td></tr><tr><td>354</td><td>bigbench/similarities_abstraction</td><td>MultipleChoice</td></tr><tr><td>355</td><td>bigbench/novel_concepts</td><td>MultipleChoice</td></tr><tr><td>356</td><td>bigbench/snarks</td><td>MultipleChoice</td></tr><tr><td>357</td><td>bigbench/abstract_narrative_understanding</td><td>MultipleChoice</td></tr><tr><td>358</td><td>bigbench/social_iqa</td><td>MultipleChoice</td></tr><tr><td>359</td><td>bigbench/phrase_relatedness</td><td>MultipleChoice</td></tr><tr><td>360</td><td>bigbench/physics</td><td>MultipleChoice</td></tr><tr><td>361</td><td>bigbench/gre_readng_comprehension</td><td>MultipleChoice</td></tr><tr><td>362</td><td>bigbench/logical_sequence</td><td>MultipleChoice</td></tr><tr><td>363</td><td>bigbench/winowhy</td><td>MultipleChoice</td></tr><tr><td>364</td><td>bigbench/movie_dialog same_or_dif ferent</td><td>MultipleChoice</td></tr><tr><td>365</td><td>bigbench/riddle_sense</td><td>MultipleChoice</td></tr><tr><td>366</td><td>bigbench/metaphor_understanding</td><td>MultipleChoice</td></tr><tr><td>367</td><td>bigbench/moral_permissibility</td><td>MultipleChoice</td></tr><tr><td>368</td><td>bigbench/nonsense_words_grammar</td><td>MultipleChoice</td></tr><tr><td>369</td><td>bigbench/bbqann</td><td>MultipleChoice</td></tr><tr><td>370</td><td>bigbench/physical_intuition</td><td>MultipleChoice</td></tr><tr><td>371</td><td>bigbench/navigate</td><td>MultipleChoice</td></tr><tr><td>372</td><td>bigbench/reasoningabout_colored_objects</td><td>MultipleChoice</td></tr><tr><td>373</td><td>bigbench/metaphor_boolean</td><td>MultipleChoice</td></tr><tr><td>374</td><td>bigbench/analytic_entailment</td><td>MultipleChoice</td></tr><tr><td>375</td><td>bigbench/mnist_ascii</td><td>MultipleChoice</td></tr><tr><td>376</td><td>bigbench/misconceptions</td><td>MultipleChoice</td></tr><tr><td>377</td><td>bigbench/authorship_verify</td><td>MultipleChoice</td></tr><tr><td>378</td><td>bigbench/social_support</td><td>MultipleChoice</td></tr><tr><td>379</td><td>bigbench/tracking_shuffled_objects</td><td>MultipleChoice</td></tr><tr><td>380</td><td>bigbench/analogicalSimilarity</td><td>MultipleChoice</td></tr><tr><td>381</td><td>bigbench/figure_of_speech_detection</td><td>MultipleChoice</td></tr></tbody><tfoot><tr><td colspan="3">Continued on next page</td></tr></tfoot></table>
<table><thead><tr><th></th><th>preprocessing</th><th>task type</th></tr></thead><tbody><tr><td>430</td><td>blimp/animate_subject_passive (Warstadt et al., 2019)</td><td>MultipleChoice</td></tr><tr><td>431</td><td>blimp/wh_vs (Warstadt et al., 2019)</td><td>MultipleChoice</td></tr><tr><td>432</td><td>blimp/wh_questions_subject震 gap_long_distance (Warstadt et al., 2019)</td><td>MultipleChoice</td></tr><tr><td>433</td><td>blimp/sentential_subject_island (Warstadt et al., 2019)</td><td>MultipleChoice</td></tr><tr><td>434</td><td>blimp/wh_questions_object震 gap (Warstadt et al., 2019)</td><td>MultipleChoice</td></tr><tr><td>435</td><td>blimp/principle_A_domain_2 (Warstadt et al., 2019)</td><td>MultipleChoice</td></tr><tr><td>436</td><td>cos_e/v1.0 (Rajani et al., 2019)</td><td>MultipleChoice</td></tr><tr><td>437</td><td>cosmos_qa (Huang et al., 2019)</td><td>MultipleChoice</td></tr><tr><td>438</td><td>dream (Sun et al., 2019)</td><td>MultipleChoice</td></tr><tr><td>439</td><td>openbookqa (Mihaylov et al., 2018)</td><td>MultipleChoice</td></tr><tr><td>440</td><td>qasc (Khot et al., 2020)</td><td>MultipleChoice</td></tr><tr><td>441</td><td>quartz (Tafjord et al., "2019")</td><td>MultipleChoice</td></tr><tr><td>442</td><td>quail (Rogers et al., 2020)</td><td>MultipleChoice</td></tr><tr><td>443</td><td>head_qa/en (Vilares and Gomez-Rodriguez, 2019)</td><td>MultipleChoice</td></tr><tr><td>444</td><td>sciq (Johannes Welbl, 2017)</td><td>MultipleChoice</td></tr><tr><td>445</td><td>social_i_qa</td><td>MultipleChoice</td></tr><tr><td>446</td><td>wiki_hop/original (Welbl et al., 2018)</td><td>MultipleChoice</td></tr><tr><td>447</td><td>wiqa (Tandon et al., 2019)</td><td>MultipleChoice</td></tr><tr><td>448</td><td>piqa (Bisk et al., 2020)</td><td>MultipleChoice</td></tr><tr><td>449</td><td>hellaswag (Zellers et al., 2019)</td><td>MultipleChoice</td></tr><tr><td>450</td><td>super_glue/copa (Roemmele et al., 2011)</td><td>MultipleChoice</td></tr><tr><td>451</td><td>balanced-copa (Kavumba et al., 2019)</td><td>MultipleChoice</td></tr><tr><td>452</td><td>e-CARE</td><td>MultipleChoice</td></tr><tr><td>453</td><td>art (Chandra et al., 2020)</td><td>MultipleChoice</td></tr><tr><td>454</td><td>mmlu/nutrition (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>455</td><td>mmlu/college_medicine (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>456</td><td>mmlu/philosophy (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>457</td><td>mmlu/global_facts (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>458</td><td>mmlu/college_mathematics (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>459</td><td>mmlu/college_computer_science (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>460</td><td>mmlu/college_chemistry (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>461</td><td>mmlu/college_biology (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>462</td><td>mmlu/clinical_knowledge (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>463</td><td>mmlu/business_ethics (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>464</td><td>mmlu/astronomy (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>465</td><td>mmlu/machine_learning (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>466</td><td>mmlu/moral_scenarios (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>467</td><td>mmlu/sociology (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>468</td><td>mmlu/us_foreign_policy (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>469</td><td>mmlu/virology (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>470</td><td>mmlu/world_religions (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>471</td><td>mmlu/prehistory (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>472</td><td>mmlu/professional_accounting (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>473</td><td>mmlu/professional_law (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>474</td><td>mmlu/professional_medicine (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>475</td><td>mmlu/professional.psychology (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>476</td><td>mmlu/electrical_engineering (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>477</td><td>mmlu/elementary_mathematics (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr></tbody><tfoot><tr><td colspan="3">Continued on next page</td></tr></tfoot></table>
<table><thead><tr><th></th><th>preprocessing</th><th>task type</th></tr></thead><tbody><tr><td>478</td><td>mmlu/anatomy (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>479</td><td>mmlu/abstract_algebra (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>480</td><td>mmlu/medical_genetics (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>481</td><td>mmlu/miscellaneous (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>482</td><td>mmlu/logical_fallacies (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>483</td><td>mmlu/jurisprudence (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>484</td><td>mmlu/computer_security (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>485</td><td>mmlu/international_law (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>486</td><td>mmlu/human sexuality (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>487</td><td>mmlu/human_aging (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>488</td><td>mmlu/high_school_world_history (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>489</td><td>mmlu/college_physics (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>490</td><td>mmlu/high_school_us_history (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>491</td><td>mmlu/high_school_statistics (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>492</td><td>mmlu/conceptual_physics (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>493</td><td>mmlu/high_school.psychology (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>494</td><td>mmlu/high_school_physics (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>495</td><td>mmlu/high_school_microeconomics (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>496</td><td>mmlu/high_school (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>497</td><td>mmlu/econometrics (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>498</td><td>mmlu/high_school_macroeconomics (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>499</td><td>mmlu/high_school_government_and_politics (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>500</td><td>mmlu/high_school_geography (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>501</td><td>mmlu/high_school_european_history (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>502</td><td>mmlu/high_school_computer_science (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>503</td><td>mmlu/high_school_chemistry (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>504</td><td>mmlu/high_school_biology (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>505</td><td>mmlu/marketing (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>506</td><td>mmlu/management (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>507</td><td>mmlu/moral_disputes (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>508</td><td>mmlu/formal_logic (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>509</td><td>mmlu/security_studies (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>510</td><td>mmlu/public_relations (Hendrycks et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>511</td><td>winogrande/winogrande_xl (ai2, 2019)</td><td>MultipleChoice</td></tr><tr><td>512</td><td>codah/codah (Chen et al., 2019a)</td><td>MultipleChoice</td></tr><tr><td>513</td><td>ai2_arc/ARC-Challenge/challenge (Clark et al., 2018)</td><td>MultipleChoice</td></tr><tr><td>514</td><td>ai2_arc/ARC-Easy/challenge (Clark et al., 2018)</td><td>MultipleChoice</td></tr><tr><td>515</td><td>definite_pronoun_resolve (Rahman and Ng, 2012)</td><td>MultipleChoice</td></tr><tr><td>516</td><td>swag/regular (Zellers et al., 2018)</td><td>MultipleChoice</td></tr><tr><td>517</td><td>math_qa</td><td>MultipleChoice</td></tr><tr><td>518</td><td>lex_glue/case_hold (Zheng et al., 2021)</td><td>MultipleChoice</td></tr><tr><td>519</td><td>commonsense_qa (Talmor et al., 2019)</td><td>MultipleChoice</td></tr><tr><td>520</td><td>discosense</td><td>MultipleChoice</td></tr><tr><td>521</td><td>medmcqa (Pal et al., 2022)</td><td>MultipleChoice</td></tr><tr><td>522</td><td>aqua_rat/tokenized (ACL, 2017)</td><td>MultipleChoice</td></tr><tr><td>523</td><td>logiqa (Liu et al., 2020)</td><td>MultipleChoice</td></tr><tr><td>524</td><td>cyclic_multiplechoice (Kejriwal and Shen, 2020)</td><td>MultipleChoice</td></tr><tr><td>525</td><td>arct (Habernal et al., 2018)</td><td>MultipleChoice</td></tr></tbody><tfoot><tr><td colspan="3">Continued on next page</td></tr></tfoot></table>
<table>
  <thead>
    <tr>
      <th></th>
      <th>preprocessing</th>
      <th>task type</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>526</td>
      <td>onestop_qa (Berzak et al., 2020)</td>
      <td>MultipleChoice</td>
    </tr>
    <tr>
      <td>527</td>
      <td>moral_stories/full (Emelin et al., 2021)</td>
      <td>MultipleChoice</td>
    </tr>
    <tr>
      <td>528</td>
      <td>prost (Aroca-Ouellette et al., 2021)</td>
      <td>MultipleChoice</td>
    </tr>
    <tr>
      <td>529</td>
      <td>webgpt_comparisons (Nakano et al., 2021)</td>
      <td>MultipleChoice</td>
    </tr>
    <tr>
      <td>530</td>
      <td>synthetic-instruct-gptj-pairwise</td>
      <td>MultipleChoice</td>
    </tr>
    <tr>
      <td>531</td>
      <td>wouldyourather</td>
      <td>MultipleChoice</td>
    </tr>
    <tr>
      <td>532</td>
      <td>summarize_from_feedback/comparisons (Stiennon et al., 2020)</td>
      <td>MultipleChoice</td>
    </tr>
    <tr>
      <td>533</td>
      <td>SHP (Ethayarajh et al., 2023)</td>
      <td>MultipleChoice</td>
    </tr>
    <tr>
      <td>534</td>
      <td>MedQA-USMLE-4-options-hf</td>
      <td>MultipleChoice</td>
    </tr>
    <tr>
      <td>535</td>
      <td>wikimedqa/medwiki (Sileo et al., 2023)</td>
      <td>MultipleChoice</td>
    </tr>
    <tr>
      <td>536</td>
      <td>cicero (Ghosal et al., 2022)</td>
      <td>MultipleChoice</td>
    </tr>
    <tr>
      <td>537</td>
      <td>mutual (Cui et al., 2020)</td>
      <td>MultipleChoice</td>
    </tr>
    <tr>
      <td>538</td>
      <td>NeQA</td>
      <td>MultipleChoice</td>
    </tr>
    <tr>
      <td>539</td>
      <td>quote-repetition</td>
      <td>MultipleChoice</td>
    </tr>
    <tr>
      <td>540</td>
      <td>redefine-math</td>
      <td>MultipleChoice</td>
    </tr>
    <tr>
      <td>541</td>
      <td>implicatures (George and Mamidi, 2020)</td>
      <td>MultipleChoice</td>
    </tr>
    <tr>
      <td>542</td>
      <td>race/high (Lai et al., 2017)</td>
      <td>MultipleChoice</td>
    </tr>
    <tr>
      <td>543</td>
      <td>race/middle (Lai et al., 2017)</td>
      <td>MultipleChoice</td>
    </tr>
    <tr>
      <td>544</td>
      <td>race-c (Liang et al., 2019)</td>
      <td>MultipleChoice</td>
    </tr>
    <tr>
      <td>545</td>
      <td>spartqa-mchoice (Mirzaee et al., 2021)</td>
      <td>MultipleChoice</td>
    </tr>
    <tr>
      <td>546</td>
      <td>riddle_sense (Lin et al., 2021a)</td>
      <td>MultipleChoice</td>
    </tr>
    <tr>
      <td>547</td>
      <td>reclor (Yu et al., 2020b)</td>
      <td>MultipleChoice</td>
    </tr>
    <tr>
      <td>548</td>
      <td>ScienceQA_text_only (Saikh et al., 2022)</td>
      <td>MultipleChoice</td>
    </tr>
    <tr>
      <td>549</td>
      <td>ekar_english</td>
      <td>MultipleChoice</td>
    </tr>
    <tr>
      <td>550</td>
      <td>path-naturalness-prediction</td>
      <td>MultipleChoice</td>
    </tr>
    <tr>
      <td>551</td>
      <td>cloth</td>
      <td>MultipleChoice</td>
    </tr>
    <tr>
      <td>552</td>
      <td>dgen</td>
      <td>MultipleChoice</td>
    </tr>
    <tr>
      <td>553</td>
      <td>oasst1_pairwise_rlhf_reward</td>
      <td>MultipleChoice</td>
    </tr>
    <tr>
      <td>554</td>
      <td>conll2003/poseloop (Tjong Kim Sang and De Meulder, 2003)</td>
      <td>TokenClassification</td>
    </tr>
    <tr>
      <td>555</td>
      <td>conll2003/chunkeloop (Tjong Kim Sang and De Meulder, 2003)</td>
      <td>TokenClassification</td>
    </tr>
    <tr>
      <td>556</td>
      <td>conll2003/nereloop (Tjong Kim Sang and De Meulder, 2003)</td>
      <td>TokenClassification</td>
    </tr>
    <tr>
      <td>557</td>
      <td>wnut_17/wnut_17 (Derczynski et al., 2017)</td>
      <td>TokenClassification</td>
    </tr>
    <tr>
      <td>558</td>
      <td>ncbi_disease/ncbi_disease (Dogan et al., 2014)</td>
      <td>TokenClassification</td>
    </tr>
    <tr>
      <td>559</td>
      <td>acronym_identification (Veyseh et al., 2020)</td>
      <td>TokenClassification</td>
    </tr>
    <tr>
      <td>560</td>
      <td>jnlpba/jnlpba (Kim et al., 2004)</td>
      <td>TokenClassification</td>
    </tr>
    <tr>
      <td>561</td>
      <td>species_800/species_800 (Pafilis et al., 2013)</td>
      <td>TokenClassification</td>
    </tr>
    <tr>
      <td>562</td>
      <td>ontonotes_english (Tjong Kim Sang and De Meulder, 2003)</td>
      <td>TokenClassification</td>
    </tr>
    <tr>
      <td>563</td>
      <td>universal_dependencies/en_partout/deprel (Zeman et al., 2020)</td>
      <td>TokenClassification</td>
    </tr>
    <tr>
      <td>564</td>
      <td>universal_dependencies/en_lines/deprel (Zeman et al., 2020)</td>
      <td>TokenClassification</td>
    </tr>
    <tr>
      <td>565</td>
      <td>universal_dependencies/en_gumreddit/deprel (Zeman et al., 2020)</td>
      <td>TokenClassification</td>
    </tr>
    <tr>
      <td>566</td>
      <td>universal_dependencies/en_egl/deprel (Zeman et al., 2020)</td>
      <td>TokenClassification</td>
    </tr>
    <tr>
      <td>567</td>
      <td>universal_dependencies/en_ewt/deprel (Zeman et al., 2020)</td>
      <td>TokenClassification</td>
    </tr>
    <tr>
      <td>568</td>
      <td>universal_dependencies/en_gum/deprel (Zeman et al., 2020)</td>
      <td>TokenClassification</td>
    </tr>
  </tbody>
</table>
B Model Recycling results

<table><thead><tr><th>model_name</th><th>deberta-v3-base</th><th>+tasksource</th></tr></thead><tbody><tr><td>avg</td><td>79.04</td><td><strong>80.73</strong></td></tr><tr><td>mnli (linear probe)</td><td>-</td><td><strong>93.73</strong></td></tr><tr><td>20_newsgroup</td><td>86.41</td><td><strong>86.46</strong></td></tr><tr><td>ag_news</td><td>90.44</td><td><strong>90.67</strong></td></tr><tr><td>amazonreviews_multi</td><td>66.86</td><td><strong>66.90</strong></td></tr><tr><td>anli</td><td>58.78</td><td><strong>60.38</strong></td></tr><tr><td>boolq</td><td>82.99</td><td><strong>85.66</strong></td></tr><tr><td>cb</td><td>75.00</td><td><strong>82.14</strong></td></tr><tr><td>cola</td><td>86.57</td><td><strong>87.15</strong></td></tr><tr><td>copa</td><td>58.40</td><td><strong>81.00</strong></td></tr><tr><td>dbpedia</td><td><strong>79.43</strong></td><td>79.20</td></tr><tr><td>esnli</td><td>91.93</td><td><strong>91.54</strong></td></tr><tr><td>financialPhrasebank</td><td>84.48</td><td><strong>85.20</strong></td></tr><tr><td>imdb</td><td>94.49</td><td><strong>94.67</strong></td></tr><tr><td>isear</td><td>71.86</td><td><strong>71.90</strong></td></tr><tr><td>mnli_mismatched</td><td>89.78</td><td><strong>91.14</strong></td></tr><tr><td>mrpc</td><td><strong>89.20</strong></td><td>88.73</td></tr><tr><td>multirc</td><td>62.26</td><td><strong>63.82</strong></td></tr><tr><td>poem_sentiment</td><td>86.73</td><td><strong>92.31</strong></td></tr><tr><td>qnli</td><td>93.51</td><td><strong>93.72</strong></td></tr><tr><td>qqp</td><td>91.79</td><td><strong>91.92</strong></td></tr><tr><td>rotten_tomatoes</td><td>90.42</td><td><strong>90.99</strong></td></tr><tr><td>rte</td><td>82.35</td><td><strong>90.61</strong></td></tr><tr><td>sst2</td><td>95.06</td><td><strong>95.41</strong></td></tr><tr><td>sst_5bins</td><td>56.98</td><td><strong>58.60</strong></td></tr><tr><td>stsb</td><td>90.28</td><td><strong>91.81</strong></td></tr><tr><td>trec_coarse</td><td>97.76</td><td><strong>96.80</strong></td></tr><tr><td>trec_fine</td><td><strong>91.02</strong></td><td>90.80</td></tr><tr><td>tweet_ev_emoji</td><td>46.19</td><td><strong>47.82</strong></td></tr><tr><td>tweet_ev_emotion</td><td>83.95</td><td><strong>85.71</strong></td></tr><tr><td>tweet_ev_hate</td><td>56.21</td><td><strong>57.47</strong></td></tr><tr><td>tweet_ev_irony</td><td>79.82</td><td><strong>83.04</strong></td></tr><tr><td>tweet_ev_offensive</td><td>85.06</td><td><strong>85.23</strong></td></tr><tr><td>tweet_ev_sentiment</td><td>71.80</td><td><strong>72.01</strong></td></tr><tr><td>wic</td><td><strong>71.21</strong></td><td>69.44</td></tr><tr><td>wnli</td><td><strong>70.21</strong></td><td>67.61</td></tr><tr><td>wsc</td><td>64.09</td><td><strong>66.35</strong></td></tr><tr><td>yahoo</td><td>72.03</td><td><strong>72.07</strong></td></tr></tbody></table>