
# Dataset Card for KILT

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** https://ai.facebook.com/tools/kilt/
- **Repository:** https://github.com/facebookresearch/KILT
- **Paper:** https://arxiv.org/abs/2009.02252
- **Leaderboard:** https://eval.ai/web/challenges/challenge-page/689/leaderboard/
- **Point of Contact:** [Needs More Information]

### Dataset Summary

KILT has been built from 11 datasets representing 5 types of tasks:

- Fact-checking
- Entity linking
- Slot filling
- Open domain QA
- Dialog generation

All these datasets have been grounded in a single pre-processed Wikipedia dump, allowing for fairer and more consistent evaluation as well as enabling new task setups such as multitask and transfer learning with minimal effort. KILT also provides tools to analyze and understand the predictions made by models, as well as the evidence they provide for their predictions.

#### Loading the KILT knowledge source and task data

The original KILT [release](https://github.com/facebookresearch/KILT) only provides question IDs for the TriviaQA task. Using the full dataset requires mapping those back to the TriviaQA questions, which can be done as follows: 

```python
from datasets import load_dataset

# Get the pre-processed Wikipedia knowledge source for kild
kilt_wiki = load_dataset("kilt_wikipedia")

# Get the KILT task datasets
kilt_triviaqa = load_dataset("kilt_tasks", name="triviaqa_support_only")

# Most tasks in KILT already have all required data, but KILT-TriviaQA
# only provides the question IDs, not the questions themselves.
# Thankfully, we can get the original TriviaQA data with:
trivia_qa = load_dataset('trivia_qa', 'unfiltered.nocontext')

# The KILT IDs can then be mapped to the TriviaQA questions with:
triviaqa_map = {}

def add_missing_data(x, trivia_qa_subset, triviaqa_map):
    i = triviaqa_map[x['id']]
    x['input'] = trivia_qa_subset[i]['question']
    x['output']['original_answer'] = trivia_qa_subset[i]['answer']['value']
    return x
    
for k in ['train', 'validation', 'test']:
    triviaqa_map = dict([(q_id, i) for i, q_id in enumerate(trivia_qa[k]['question_id'])])
    kilt_triviaqa[k] = kilt_triviaqa[k].filter(lambda x: x['id'] in triviaqa_map)
    kilt_triviaqa[k] = kilt_triviaqa[k].map(add_missing_data, fn_kwargs=dict(trivia_qa_subset=trivia_qa[k], triviaqa_map=triviaqa_map))
```

### Supported Tasks and Leaderboards

The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.

The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).

### Languages

All tasks are in English (`en`).

## Dataset Structure

### Data Instances

An example of open-domain QA from the Natural Questions `nq` configuration looks as follows:
```
{'id': '-5004457603684974952',
 'input': 'who is playing the halftime show at super bowl 2016',
 'meta': {'left_context': '',
  'mention': '',
  'obj_surface': [],
  'partial_evidence': [],
  'right_context': '',
  'sub_surface': [],
  'subj_aliases': [],
  'template_questions': []},
 'output': [{'answer': 'Coldplay',
   'meta': {'score': 0},
   'provenance': [{'bleu_score': 1.0,
     'end_character': 186,
     'end_paragraph_id': 1,
     'meta': {'annotation_id': '-1',
      'evidence_span': [],
      'fever_page_id': '',
      'fever_sentence_id': -1,
      'yes_no_answer': ''},
     'section': 'Section::::Abstract.',
     'start_character': 178,
     'start_paragraph_id': 1,
     'title': 'Super Bowl 50 halftime show',
     'wikipedia_id': '45267196'}]},
  {'answer': 'Beyoncé',
   'meta': {'score': 0},
   'provenance': [{'bleu_score': 1.0,
     'end_character': 224,
     'end_paragraph_id': 1,
     'meta': {'annotation_id': '-1',
      'evidence_span': [],
      'fever_page_id': '',
      'fever_sentence_id': -1,
      'yes_no_answer': ''},
     'section': 'Section::::Abstract.',
     'start_character': 217,
     'start_paragraph_id': 1,
     'title': 'Super Bowl 50 halftime show',
     'wikipedia_id': '45267196'}]},
  {'answer': 'Bruno Mars',
   'meta': {'score': 0},
   'provenance': [{'bleu_score': 1.0,
     'end_character': 239,
     'end_paragraph_id': 1,
     'meta': {'annotation_id': '-1',
      'evidence_span': [],
      'fever_page_id': '',
      'fever_sentence_id': -1,
      'yes_no_answer': ''},
     'section': 'Section::::Abstract.',
     'start_character': 229,
     'start_paragraph_id': 1,
     'title': 'Super Bowl 50 halftime show',
     'wikipedia_id': '45267196'}]},
  {'answer': 'Coldplay with special guest performers Beyoncé and Bruno Mars',
   'meta': {'score': 0},
   'provenance': []},
  {'answer': 'British rock group Coldplay with special guest performers Beyoncé and Bruno Mars',
   'meta': {'score': 0},
   'provenance': []},
  {'answer': '',
   'meta': {'score': 0},
   'provenance': [{'bleu_score': 0.9657992720603943,
     'end_character': 341,
     'end_paragraph_id': 1,
     'meta': {'annotation_id': '2430977867500315580',
      'evidence_span': [],
      'fever_page_id': '',
      'fever_sentence_id': -1,
      'yes_no_answer': 'NONE'},
     'section': 'Section::::Abstract.',
     'start_character': 0,
     'start_paragraph_id': 1,
     'title': 'Super Bowl 50 halftime show',
     'wikipedia_id': '45267196'}]},
  {'answer': '',
   'meta': {'score': 0},
   'provenance': [{'bleu_score': -1.0,
     'end_character': -1,
     'end_paragraph_id': 1,
     'meta': {'annotation_id': '-1',
      'evidence_span': ['It was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars',
       'It was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars, who previously had headlined the Super Bowl XLVII and Super Bowl XLVIII halftime shows, respectively.',
       "The Super Bowl 50 Halftime Show took place on February 7, 2016, at Levi's Stadium in Santa Clara, California as part of Super Bowl 50. It was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars",
       "The Super Bowl 50 Halftime Show took place on February 7, 2016, at Levi's Stadium in Santa Clara, California as part of Super Bowl 50. It was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars,"],
      'fever_page_id': '',
      'fever_sentence_id': -1,
      'yes_no_answer': ''},
     'section': 'Section::::Abstract.',
     'start_character': -1,
     'start_paragraph_id': 1,
     'title': 'Super Bowl 50 halftime show',
     'wikipedia_id': '45267196'}]}]}
```

### Data Fields

Examples from all configurations have the following features:

- `input`: a `string` feature representing the query.
- `output`: a `list` of features each containing information for an answer, made up of:
  - `answer`: a `string` feature representing a possible answer.
  - `provenance`: a `list` of features representing Wikipedia passages that support the `answer`, denoted by:
    - `title`: a `string` feature, the title of the Wikipedia article the passage was retrieved from.
    - `section`: a `string` feature, the title of the section in Wikipedia article.
    - `wikipedia_id`: a `string` feature, a unique identifier for the Wikipedia article.
    - `start_character`: a `int32` feature.
    - `start_paragraph_id`: a `int32` feature.
    - `end_character`: a `int32` feature.
    - `end_paragraph_id`: a `int32` feature.


### Data Splits

The configurations have the following splits:

|             | Train       | Validation  | Test        |
| ----------- | ----------- | ----------- | ----------- |
| triviaqa    | 61844         | 5359  | 6586  |
| fever       | 104966        | 10444         | 10100         |
| aidayago2   | 18395         | 4784  | 4463  |
| wned   | | 3396 | 3376 |
| cweb   | | 5599 | 5543 |
| trex   | 2284168       | 5000  | 5000  |
| structured_zeroshot    | 147909        | 3724  | 4966  |
| nq     | 87372         | 2837  | 1444  |
| hotpotqa       | 88869         | 5600  | 5569  |
| eli5   | 272634        | 1507  | 600   |
| wow    | 94577         | 3058  | 2944  |


## Dataset Creation

### Curation Rationale

[Needs More Information]

### Source Data

#### Initial Data Collection and Normalization

[Needs More Information]

#### Who are the source language producers?

[Needs More Information]

### Annotations

#### Annotation process

[Needs More Information]

#### Who are the annotators?

[Needs More Information]

### Personal and Sensitive Information

[Needs More Information]

## Considerations for Using the Data

### Social Impact of Dataset

[Needs More Information]

### Discussion of Biases

[Needs More Information]

### Other Known Limitations

[Needs More Information]

## Additional Information

### Dataset Curators

[Needs More Information]

### Licensing Information

[Needs More Information]

### Citation Information

Cite as:
```
@inproceedings{kilt_tasks,
  author    = {Fabio Petroni and
               Aleksandra Piktus and
               Angela Fan and
               Patrick S. H. Lewis and
               Majid Yazdani and
               Nicola De Cao and
               James Thorne and
               Yacine Jernite and
               Vladimir Karpukhin and
               Jean Maillard and
               Vassilis Plachouras and
               Tim Rockt{\"{a}}schel and
               Sebastian Riedel},
  editor    = {Kristina Toutanova and
               Anna Rumshisky and
               Luke Zettlemoyer and
               Dilek Hakkani{-}T{\"{u}}r and
               Iz Beltagy and
               Steven Bethard and
               Ryan Cotterell and
               Tanmoy Chakraborty and
               Yichao Zhou},
  title     = {{KILT:} a Benchmark for Knowledge Intensive Language Tasks},
  booktitle = {Proceedings of the 2021 Conference of the North American Chapter of
               the Association for Computational Linguistics: Human Language Technologies,
               {NAACL-HLT} 2021, Online, June 6-11, 2021},
  pages     = {2523--2544},
  publisher = {Association for Computational Linguistics},
  year      = {2021},
  url       = {https://www.aclweb.org/anthology/2021.naacl-main.200/}
}
```

### Contributions

Thanks to [@thomwolf](https://github.com/thomwolf), [@yjernite](https://github.com/yjernite) for adding this dataset.
task_categories:fill-mask task_categories:question-answering task_categories:text-classification task_categories:text-generation task_categories:text-retrieval task_categories:text2text-generation task_ids:abstractive-qa task_ids:dialogue-modeling task_ids:document-retrieval task_ids:entity-linking-retrieval task_ids:extractive-qa task_ids:fact-checking task_ids:fact-checking-retrieval task_ids:open-domain-abstractive-qa task_ids:open-domain-qa task_ids:slot-filling annotations_creators:crowdsourced annotations_creators:found annotations_creators:machine-generated language_creators:crowdsourced language_creators:found multilinguality:monolingual size_categories:100K<n<1M size_categories:10K<n<100K size_categories:1K<n<10K size_categories:1M<n<10M source_datasets:extended|natural_questions source_datasets:extended|other-aidayago source_datasets:extended|other-fever source_datasets:extended|other-hotpotqa source_datasets:extended|other-trex source_datasets:extended|other-triviaqa source_datasets:extended|other-wizardsofwikipedia source_datasets:extended|other-wned-cweb source_datasets:extended|other-wned-wiki source_datasets:extended|other-zero-shot-re source_datasets:original language:en license:mit arxiv:2009.02252 region:us
{"annotations_creators": ["crowdsourced", "found", "machine-generated"], "language_creators": ["crowdsourced", "found"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M", "10K<n<100K", "1K<n<10K", "1M<n<10M"], "source_datasets": ["extended|natural_questions", "extended|other-aidayago", "extended|other-fever", "extended|other-hotpotqa", "extended|other-trex", "extended|other-triviaqa", "extended|other-wizardsofwikipedia", "extended|other-wned-cweb", "extended|other-wned-wiki", "extended|other-zero-shot-re", "original"], "task_categories": ["fill-mask", "question-answering", "text-classification", "text-generation", "text-retrieval", "text2text-generation"], "task_ids": ["abstractive-qa", "dialogue-modeling", "document-retrieval", "entity-linking-retrieval", "extractive-qa", "fact-checking", "fact-checking-retrieval", "open-domain-abstractive-qa", "open-domain-qa", "slot-filling"], "paperswithcode_id": "kilt", "pretty_name": 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"data_files": [{"split": "train", "path": "triviaqa_support_only/train-*"}, {"split": "validation", "path": "triviaqa_support_only/validation-*"}, {"split": "test", "path": "triviaqa_support_only/test-*"}]}, {"config_name": "wned", "data_files": [{"split": "validation", "path": "wned/validation-*"}, {"split": "test", "path": "wned/test-*"}]}, {"config_name": "wow", "data_files": [{"split": "train", "path": "wow/train-*"}, {"split": "validation", "path": "wow/validation-*"}, {"split": "test", "path": "wow/test-*"}]}]}
TAGS
#task_categories-fill-mask #task_categories-question-answering #task_categories-text-classification #task_categories-text-generation #task_categories-text-retrieval #task_categories-text2text-generation #task_ids-abstractive-qa #task_ids-dialogue-modeling #task_ids-document-retrieval #task_ids-entity-linking-retrieval #task_ids-extractive-qa #task_ids-fact-checking #task_ids-fact-checking-retrieval #task_ids-open-domain-abstractive-qa #task_ids-open-domain-qa #task_ids-slot-filling #annotations_creators-crowdsourced #annotations_creators-found #annotations_creators-machine-generated #language_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #size_categories-10K<n<100K #size_categories-1K<n<10K #size_categories-1M<n<10M #source_datasets-extended|natural_questions #source_datasets-extended|other-aidayago #source_datasets-extended|other-fever #source_datasets-extended|other-hotpotqa #source_datasets-extended|other-trex #source_datasets-extended|other-triviaqa #source_datasets-extended|other-wizardsofwikipedia #source_datasets-extended|other-wned-cweb #source_datasets-extended|other-wned-wiki #source_datasets-extended|other-zero-shot-re #source_datasets-original #language-English #license-mit #arxiv-2009.02252 #region-us 

Dataset Card for KILT
=====================


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ Personal and Sensitive Information
* Considerations for Using the Data
	+ Social Impact of Dataset
	+ Discussion of Biases
	+ Other Known Limitations
* Additional Information
	+ Dataset Curators
	+ Licensing Information
	+ Citation Information
	+ Contributions


Dataset Description
-------------------


* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact:


### Dataset Summary


KILT has been built from 11 datasets representing 5 types of tasks:


* Fact-checking
* Entity linking
* Slot filling
* Open domain QA
* Dialog generation


All these datasets have been grounded in a single pre-processed Wikipedia dump, allowing for fairer and more consistent evaluation as well as enabling new task setups such as multitask and transfer learning with minimal effort. KILT also provides tools to analyze and understand the predictions made by models, as well as the evidence they provide for their predictions.


#### Loading the KILT knowledge source and task data


The original KILT release only provides question IDs for the TriviaQA task. Using the full dataset requires mapping those back to the TriviaQA questions, which can be done as follows:


### Supported Tasks and Leaderboards


The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.


The current best performing models can be found here.


### Languages


All tasks are in English ('en').


Dataset Structure
-----------------


### Data Instances


An example of open-domain QA from the Natural Questions 'nq' configuration looks as follows:


### Data Fields


Examples from all configurations have the following features:


* 'input': a 'string' feature representing the query.
* 'output': a 'list' of features each containing information for an answer, made up of:
	+ 'answer': a 'string' feature representing a possible answer.
	+ 'provenance': a 'list' of features representing Wikipedia passages that support the 'answer', denoted by:
		- 'title': a 'string' feature, the title of the Wikipedia article the passage was retrieved from.
		- 'section': a 'string' feature, the title of the section in Wikipedia article.
		- 'wikipedia\_id': a 'string' feature, a unique identifier for the Wikipedia article.
		- 'start\_character': a 'int32' feature.
		- 'start\_paragraph\_id': a 'int32' feature.
		- 'end\_character': a 'int32' feature.
		- 'end\_paragraph\_id': a 'int32' feature.


### Data Splits


The configurations have the following splits:



Dataset Creation
----------------


### Curation Rationale


### Source Data


#### Initial Data Collection and Normalization


#### Who are the source language producers?


### Annotations


#### Annotation process


#### Who are the annotators?


### Personal and Sensitive Information


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


### Discussion of Biases


### Other Known Limitations


Additional Information
----------------------


### Dataset Curators


### Licensing Information


Cite as:


### Contributions


Thanks to @thomwolf, @yjernite for adding this dataset.



### Dataset Summary


KILT has been built from 11 datasets representing 5 types of tasks:


* Fact-checking
* Entity linking
* Slot filling
* Open domain QA
* Dialog generation


All these datasets have been grounded in a single pre-processed Wikipedia dump, allowing for fairer and more consistent evaluation as well as enabling new task setups such as multitask and transfer learning with minimal effort. KILT also provides tools to analyze and understand the predictions made by models, as well as the evidence they provide for their predictions. #### Loading the KILT knowledge source and task data


The original KILT release only provides question IDs for the TriviaQA task. Using the full dataset requires mapping those back to the TriviaQA questions, which can be done as follows: ### Supported Tasks and Leaderboards


The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.


The current best performing models can be found here. ### Languages


All tasks are in English ('en').


Dataset Structure
----------------- ### Data Instances


An example of open-domain QA from the Natural Questions 'nq' configuration looks as follows: ### Data Fields


Examples from all configurations have the following features:


* 'input': a 'string' feature representing the query.
* 'output': a 'list' of features each containing information for an answer, made up of:
	+ 'answer': a 'string' feature representing a possible answer.
	+ 'provenance': a 'list' of features representing Wikipedia passages that support the 'answer', denoted by:
		- 'title': a 'string' feature, the title of the Wikipedia article the passage was retrieved from.
		- 'section': a 'string' feature, the title of the section in Wikipedia article.
		- 'wikipedia\_id': a 'string' feature, a unique identifier for the Wikipedia article.
		- 'start\_character': a 'int32' feature.
		- 'start\_paragraph\_id': a 'int32' feature.
		- 'end\_character': a 'int32' feature.
		- 'end\_paragraph\_id': a 'int32' feature. ### Data Splits


The configurations have the following splits:



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information


Cite as: ### Contributions


Thanks to @thomwolf, @yjernite for adding this dataset.
TAGS
#task_categories-fill-mask #task_categories-question-answering #task_categories-text-classification #task_categories-text-generation #task_categories-text-retrieval #task_categories-text2text-generation #task_ids-abstractive-qa #task_ids-dialogue-modeling #task_ids-document-retrieval #task_ids-entity-linking-retrieval #task_ids-extractive-qa #task_ids-fact-checking #task_ids-fact-checking-retrieval #task_ids-open-domain-abstractive-qa #task_ids-open-domain-qa #task_ids-slot-filling #annotations_creators-crowdsourced #annotations_creators-found #annotations_creators-machine-generated #language_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #size_categories-10K<n<100K #size_categories-1K<n<10K #size_categories-1M<n<10M #source_datasets-extended|natural_questions #source_datasets-extended|other-aidayago #source_datasets-extended|other-fever #source_datasets-extended|other-hotpotqa #source_datasets-extended|other-trex #source_datasets-extended|other-triviaqa #source_datasets-extended|other-wizardsofwikipedia #source_datasets-extended|other-wned-cweb #source_datasets-extended|other-wned-wiki #source_datasets-extended|other-zero-shot-re #source_datasets-original #language-English #license-mit #arxiv-2009.02252 #region-us 
 ### Dataset Summary


KILT has been built from 11 datasets representing 5 types of tasks:


* Fact-checking
* Entity linking
* Slot filling
* Open domain QA
* Dialog generation


All these datasets have been grounded in a single pre-processed Wikipedia dump, allowing for fairer and more consistent evaluation as well as enabling new task setups such as multitask and transfer learning with minimal effort. KILT also provides tools to analyze and understand the predictions made by models, as well as the evidence they provide for their predictions. #### Loading the KILT knowledge source and task data


The original KILT release only provides question IDs for the TriviaQA task. Using the full dataset requires mapping those back to the TriviaQA questions, which can be done as follows: ### Supported Tasks and Leaderboards


The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.


The current best performing models can be found here. ### Languages


All tasks are in English ('en').


Dataset Structure
----------------- ### Data Instances


An example of open-domain QA from the Natural Questions 'nq' configuration looks as follows: ### Data Fields


Examples from all configurations have the following features:


* 'input': a 'string' feature representing the query.
* 'output': a 'list' of features each containing information for an answer, made up of:
	+ 'answer': a 'string' feature representing a possible answer.
	+ 'provenance': a 'list' of features representing Wikipedia passages that support the 'answer', denoted by:
		- 'title': a 'string' feature, the title of the Wikipedia article the passage was retrieved from.
		- 'section': a 'string' feature, the title of the section in Wikipedia article.
		- 'wikipedia\_id': a 'string' feature, a unique identifier for the Wikipedia article.
		- 'start\_character': a 'int32' feature.
		- 'start\_paragraph\_id': a 'int32' feature.
		- 'end\_character': a 'int32' feature.
		- 'end\_paragraph\_id': a 'int32' feature. ### Data Splits


The configurations have the following splits:



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information


Cite as: ### Contributions


Thanks to @thomwolf, @yjernite for adding this dataset.
passage: TAGS
#task_categories-fill-mask #task_categories-question-answering #task_categories-text-classification #task_categories-text-generation #task_categories-text-retrieval #task_categories-text2text-generation #task_ids-abstractive-qa #task_ids-dialogue-modeling #task_ids-document-retrieval #task_ids-entity-linking-retrieval #task_ids-extractive-qa #task_ids-fact-checking #task_ids-fact-checking-retrieval #task_ids-open-domain-abstractive-qa #task_ids-open-domain-qa #task_ids-slot-filling #annotations_creators-crowdsourced #annotations_creators-found #annotations_creators-machine-generated #language_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #size_categories-10K<n<100K #size_categories-1K<n<10K #size_categories-1M<n<10M #source_datasets-extended|natural_questions #source_datasets-extended|other-aidayago #source_datasets-extended|other-fever #source_datasets-extended|other-hotpotqa #source_datasets-extended|other-trex #source_datasets-extended|other-triviaqa #source_datasets-extended|other-wizardsofwikipedia #source_datasets-extended|other-wned-cweb #source_datasets-extended|other-wned-wiki #source_datasets-extended|other-zero-shot-re #source_datasets-original #language-English #license-mit #arxiv-2009.02252 #region-us 
 passage: ### Dataset Summary


KILT has been built from 11 datasets representing 5 types of tasks:


* Fact-checking
* Entity linking
* Slot filling
* Open domain QA
* Dialog generation


All these datasets have been grounded in a single pre-processed Wikipedia dump, allowing for fairer and more consistent evaluation as well as enabling new task setups such as multitask and transfer learning with minimal effort. KILT also provides tools to analyze and understand the predictions made by models, as well as the evidence they provide for their predictions.#### Loading the KILT knowledge source and task data


The original KILT release only provides question IDs for the TriviaQA task. Using the full dataset requires mapping those back to the TriviaQA questions, which can be done as follows:### Supported Tasks and Leaderboards


The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.


The current best performing models can be found here.### Languages


All tasks are in English ('en').


Dataset Structure
-----------------### Data Instances


An example of open-domain QA from the Natural Questions 'nq' configuration looks as follows:### Data Fields


Examples from all configurations have the following features:


* 'input': a 'string' feature representing the query.
* 'output': a 'list' of features each containing information for an answer, made up of:
	+ 'answer': a 'string' feature representing a possible answer.
	+ 'provenance': a 'list' of features representing Wikipedia passages that support the 'answer', denoted by:
		- 'title': a 'string' feature, the title of the Wikipedia article the passage was retrieved from.
		- 'section': a 'string' feature, the title of the section in Wikipedia article.
		- 'wikipedia\_id': a 'string' feature, a unique identifier for the Wikipedia article.
		- 'start\_character': a 'int32' feature.
		- 'start\_paragraph\_id': a 'int32' feature.
		- 'end\_character': a 'int32' feature.
		- 'end\_paragraph\_id': a 'int32' feature.### Data Splits


The configurations have the following splits:



Dataset Creation
----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information


Considerations for Using the Data
---------------------------------