metadata
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
configs:
- aidayago2
- cweb
- eli5
- fever
- hotpotqa
- nq
- structured_zeroshot
- trex
- triviaqa_support_only
- wned
- wow
dataset_info:
- config_name: triviaqa_support_only
features:
- name: id
dtype: string
- name: input
dtype: string
- name: meta
struct:
- name: left_context
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- name: mention
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- name: partial_evidence
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- name: title
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- name: title
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- name: test
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download_size: 111546348
dataset_size: 79190885
- config_name: fever
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- config_name: aidayago2
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- config_name: trex
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Dataset Card for KILT
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
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 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:
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.
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: astringfeature representing the query.output: alistof features each containing information for an answer, made up of:answer: astringfeature representing a possible answer.provenance: alistof features representing Wikipedia passages that support theanswer, denoted by:title: astringfeature, the title of the Wikipedia article the passage was retrieved from.section: astringfeature, the title of the section in Wikipedia article.wikipedia_id: astringfeature, a unique identifier for the Wikipedia article.start_character: aint32feature.start_paragraph_id: aint32feature.end_character: aint32feature.end_paragraph_id: aint32feature.
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/}
}