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---
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: KILT
config_names:
- aidayago2
- cweb
- eli5
- fever
- hotpotqa
- nq
- structured_zeroshot
- trex
- triviaqa_support_only
- wned
- wow
dataset_info:
- config_name: aidayago2
features:
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dtype: string
- name: input
dtype: string
- name: meta
struct:
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dtype: string
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- name: partial_evidence
list:
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dtype: int32
- name: title
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struct:
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struct:
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struct:
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- config_name: cweb
features:
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- config_name: eli5
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- name: end_paragraph_id
dtype: int32
- name: title
dtype: string
- name: section
dtype: string
- name: wikipedia_id
dtype: string
- name: meta
struct:
- name: evidence_span
list: string
- name: obj_surface
list: string
- name: sub_surface
list: string
- name: subj_aliases
list: string
- name: template_questions
list: string
- name: output
list:
- name: answer
dtype: string
- name: meta
struct:
- name: score
dtype: int32
- name: provenance
list:
- name: bleu_score
dtype: float32
- name: start_character
dtype: int32
- name: start_paragraph_id
dtype: int32
- name: end_character
dtype: int32
- name: end_paragraph_id
dtype: int32
- name: meta
struct:
- name: fever_page_id
dtype: string
- name: fever_sentence_id
dtype: int32
- name: annotation_id
dtype: string
- name: yes_no_answer
dtype: string
- name: evidence_span
list: string
- name: section
dtype: string
- name: title
dtype: string
- name: wikipedia_id
dtype: string
splits:
- name: train
num_bytes: 72021515
num_examples: 61844
- name: validation
num_bytes: 6824398
num_examples: 5359
- name: test
num_bytes: 340692
num_examples: 6586
download_size: 31946196
dataset_size: 79186605
- config_name: wned
features:
- name: id
dtype: string
- name: input
dtype: string
- name: meta
struct:
- name: left_context
dtype: string
- name: mention
dtype: string
- name: right_context
dtype: string
- name: partial_evidence
list:
- name: start_paragraph_id
dtype: int32
- name: end_paragraph_id
dtype: int32
- name: title
dtype: string
- name: section
dtype: string
- name: wikipedia_id
dtype: string
- name: meta
struct:
- name: evidence_span
list: string
- name: obj_surface
list: string
- name: sub_surface
list: string
- name: subj_aliases
list: string
- name: template_questions
list: string
- name: output
list:
- name: answer
dtype: string
- name: meta
struct:
- name: score
dtype: int32
- name: provenance
list:
- name: bleu_score
dtype: float32
- name: start_character
dtype: int32
- name: start_paragraph_id
dtype: int32
- name: end_character
dtype: int32
- name: end_paragraph_id
dtype: int32
- name: meta
struct:
- name: fever_page_id
dtype: string
- name: fever_sentence_id
dtype: int32
- name: annotation_id
dtype: string
- name: yes_no_answer
dtype: string
- name: evidence_span
list: string
- name: section
dtype: string
- name: title
dtype: string
- name: wikipedia_id
dtype: string
splits:
- name: validation
num_bytes: 12659518
num_examples: 3396
- name: test
num_bytes: 13080824
num_examples: 3376
download_size: 3608615
dataset_size: 25740342
- config_name: wow
features:
- name: id
dtype: string
- name: input
dtype: string
- name: meta
struct:
- name: left_context
dtype: string
- name: mention
dtype: string
- name: right_context
dtype: string
- name: partial_evidence
list:
- name: start_paragraph_id
dtype: int32
- name: end_paragraph_id
dtype: int32
- name: title
dtype: string
- name: section
dtype: string
- name: wikipedia_id
dtype: string
- name: meta
struct:
- name: evidence_span
list: string
- name: obj_surface
list: string
- name: sub_surface
list: string
- name: subj_aliases
list: string
- name: template_questions
list: string
- name: output
list:
- name: answer
dtype: string
- name: meta
struct:
- name: score
dtype: int32
- name: provenance
list:
- name: bleu_score
dtype: float32
- name: start_character
dtype: int32
- name: start_paragraph_id
dtype: int32
- name: end_character
dtype: int32
- name: end_paragraph_id
dtype: int32
- name: meta
struct:
- name: fever_page_id
dtype: string
- name: fever_sentence_id
dtype: int32
- name: annotation_id
dtype: string
- name: yes_no_answer
dtype: string
- name: evidence_span
list: string
- name: section
dtype: string
- name: title
dtype: string
- name: wikipedia_id
dtype: string
splits:
- name: train
num_bytes: 41873570
num_examples: 63734
- name: validation
num_bytes: 2022128
num_examples: 3054
- name: test
num_bytes: 1340818
num_examples: 2944
download_size: 52647339
dataset_size: 45236516
configs:
- config_name: aidayago2
data_files:
- split: train
path: aidayago2/train-*
- split: validation
path: aidayago2/validation-*
- split: test
path: aidayago2/test-*
- config_name: cweb
data_files:
- split: validation
path: cweb/validation-*
- split: test
path: cweb/test-*
- config_name: fever
data_files:
- split: train
path: fever/train-*
- split: validation
path: fever/validation-*
- split: test
path: fever/test-*
- config_name: nq
data_files:
- split: train
path: nq/train-*
- split: validation
path: nq/validation-*
- split: test
path: nq/test-*
default: true
- config_name: triviaqa_support_only
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-*
---
# 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. |