Dataset Card for KILT

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: 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, @yjernite for adding this dataset.

Models trained or fine-tuned on kilt_tasks

None yet