title: Generating Benchmarks for Factuality Evaluation of Language Models
summary: Before deploying a language model (LM) within a given domain, it is important
  to measure its tendency to generate factually incorrect information in that
  domain. Existing factual generation evaluation methods focus on facts sampled
  from the LM itself, and thus do not control the set of evaluated facts and
  might under-represent rare and unlikely facts. We propose FACTOR: Factual
  Assessment via Corpus TransfORmation, a scalable approach for evaluating LM
  factuality. FACTOR automatically transforms a factual corpus of interest into a
  benchmark evaluating an LM's propensity to generate true facts from the corpus
  vs. similar but incorrect statements. We use our framework to create two
  benchmarks: Wiki-FACTOR and News-FACTOR. We show that: (i) our benchmark scores
  increase with model size and improve when the LM is augmented with retrieval;
  (ii) benchmark score correlates with perplexity, but the two metrics do not
  always agree on model ranking; and (iii) when perplexity and benchmark score
  disagree, the latter better reflects factuality in open-ended generation, as
  measured by human annotators. We make our data and code publicly available in
  https://github.com/AI21Labs/factor.
paper_authors._id: 64b0a2bb8f2b08d1aca32a83
paper_authors.name: Dor Muhlgay
paper_authors._id: 64b0a2bb8f2b08d1aca32a84
paper_authors.name: Ori Ram
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-17T11:23:15.048Z
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paper_authors.fullname: Ori Ram
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paper_authors.user: oriram
paper_authors._id: 64b0a2bb8f2b08d1aca32a85
paper_authors.name: Inbal Magar
paper_authors._id: 64b0a2bb8f2b08d1aca32a86
paper_authors.name: Yoav Levine
paper_authors._id: 64b0a2bb8f2b08d1aca32a87
paper_authors.name: Nir Ratner
paper_authors._id: 64b0a2bb8f2b08d1aca32a88
paper_authors.name: Yonatan Belinkov
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-17T11:23:50.742Z
paper_authors._id: 614c57f1ee44bcfe57b366d6
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paper_authors.fullname: Yonatan Belinkov
paper_authors.type: user
paper_authors.user: belinkov
paper_authors._id: 64b0a2bb8f2b08d1aca32a89
paper_authors.name: Omri Abend
paper_authors._id: 64b0a2bb8f2b08d1aca32a8a
paper_authors.name: Kevin Leyton-Brown
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-17T11:24:12.690Z
paper_authors._id: 63407a24b78ed99eabffeb90
paper_authors.avatarUrl: /avatars/3f9ef0c631fbd3e2cf0749853267182a.svg
paper_authors.fullname: Kevin Leyton-Brown
paper_authors.type: user
paper_authors.user: kevinlb
paper_authors._id: 64b0a2bb8f2b08d1aca32a8b
paper_authors.name: Amnon Shashua
paper_authors._id: 64b0a2bb8f2b08d1aca32a8c
paper_authors.name: Yoav Shoham
paper_title: Generating Benchmarks for Factuality Evaluation of Language Models
paper_summary: Before deploying a language model (LM) within a given domain, it is important
  to measure its tendency to generate factually incorrect information in that
  domain. Existing factual generation evaluation methods focus on facts sampled
  from the LM itself, and thus do not control the set of evaluated facts and
  might under-represent rare and unlikely facts. We propose FACTOR: Factual
  Assessment via Corpus TransfORmation, a scalable approach for evaluating LM
  factuality. FACTOR automatically transforms a factual corpus of interest into a
  benchmark evaluating an LM's propensity to generate true facts from the corpus
  vs. similar but incorrect statements. We use our framework to create two
  benchmarks: Wiki-FACTOR and News-FACTOR. We show that: (i) our benchmark scores
  increase with model size and improve when the LM is augmented with retrieval;
  (ii) benchmark score correlates with perplexity, but the two metrics do not
  always agree on model ranking; and (iii) when perplexity and benchmark score
  disagree, the latter better reflects factuality in open-ended generation, as
  measured by human annotators. We make our data and code publicly available in
  https://github.com/AI21Labs/factor.
paper_ai_keywords: factual generation language model (LM) FACTOR: Factual Assessment via Corpus TransfORmation factual corpus benchmark true facts incorrect statements model size retrieval perplexity open-ended generation human annotators