Generating Benchmarks for Factuality Evaluation of Language Models
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.
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Dor Muhlgay
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Ori Ram
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2023-07-17T11:23:15.048Z
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Ori Ram
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oriram
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Inbal Magar
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Yoav Levine
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Nir Ratner
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Yonatan Belinkov
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2023-07-17T11:23:50.742Z
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Yonatan Belinkov
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belinkov
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Omri Abend
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Kevin Leyton-Brown
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2023-07-17T11:24:12.690Z
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Kevin Leyton-Brown
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kevinlb
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Amnon Shashua
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Yoav Shoham
Generating Benchmarks for Factuality Evaluation of Language Models
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.
FACTOR evaluates language model factuality by transforming a factual corpus into a benchmark of true vs. similar incorrect statements, demonstrating improved accuracy over perplexity in large models and with retrieval augmentation.
FACTOR factual corpus benchmark language model factuality perplexity retrieval augmentation open-ended generation human annotators