Long-form factuality in large language models
Large language models (LLMs) often generate content that contains factual
errors when responding to fact-seeking prompts on open-ended topics. To
benchmark a model's long-form factuality in open domains, we first use GPT-4 to
generate LongFact, a prompt set comprising thousands of questions spanning 38
topics. We then propose that LLM agents can be used as automated evaluators for
long-form factuality through a method which we call Search-Augmented Factuality
Evaluator (SAFE). SAFE utilizes an LLM to break down a long-form response into
a set of individual facts and to evaluate the accuracy of each fact using a
multi-step reasoning process comprising sending search queries to Google Search
and determining whether a fact is supported by the search results. Furthermore,
we propose extending F1 score as an aggregated metric for long-form factuality.
To do so, we balance the percentage of supported facts in a response
(precision) with the percentage of provided facts relative to a hyperparameter
representing a user's preferred response length (recall).
  Empirically, we demonstrate that LLM agents can achieve superhuman rating
performance - on a set of ~16k individual facts, SAFE agrees with crowdsourced
human annotators 72% of the time, and on a random subset of 100 disagreement
cases, SAFE wins 76% of the time. At the same time, SAFE is more than 20 times
cheaper than human annotators. We also benchmark thirteen language models on
LongFact across four model families (Gemini, GPT, Claude, and PaLM-2), finding
that larger language models generally achieve better long-form factuality.
LongFact, SAFE, and all experimental code are available at
https://github.com/google-deepmind/long-form-factuality.
6604cf6dd8ae250868da1544
Jerry Wei
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Chengrun Yang
claimed_verified
2024-04-06T15:19:47.245Z
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/avatars/7c11abfac56a06c1122750332c40d189.svg
Chengrun Yang
user
chengrun
6604cf6dd8ae250868da1546
Xinying Song
6604cf6dd8ae250868da1547
Yifeng Lu
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Nathan Hu
6604cf6dd8ae250868da1549
Dustin Tran
6604cf6dd8ae250868da154a
Daiyi Peng
6604cf6dd8ae250868da154b
Ruibo Liu
6604cf6dd8ae250868da154c
Da Huang
6604cf6dd8ae250868da154d
Cosmo Du
6604cf6dd8ae250868da154e
Quoc V. Le
Long-form factuality in large language models
Large language models (LLMs) often generate content that contains factual
errors when responding to fact-seeking prompts on open-ended topics. To
benchmark a model's long-form factuality in open domains, we first use GPT-4 to
generate LongFact, a prompt set comprising thousands of questions spanning 38
topics. We then propose that LLM agents can be used as automated evaluators for
long-form factuality through a method which we call Search-Augmented Factuality
Evaluator (SAFE). SAFE utilizes an LLM to break down a long-form response into
a set of individual facts and to evaluate the accuracy of each fact using a
multi-step reasoning process comprising sending search queries to Google Search
and determining whether a fact is supported by the search results. Furthermore,
we propose extending F1 score as an aggregated metric for long-form factuality.
To do so, we balance the percentage of supported facts in a response
(precision) with the percentage of provided facts relative to a hyperparameter
representing a user's preferred response length (recall).
  Empirically, we demonstrate that LLM agents can achieve superhuman rating
performance - on a set of ~16k individual facts, SAFE agrees with crowdsourced
human annotators 72% of the time, and on a random subset of 100 disagreement
cases, SAFE wins 76% of the time. At the same time, SAFE is more than 20 times
cheaper than human annotators. We also benchmark thirteen language models on
LongFact across four model families (Gemini, GPT, Claude, and PaLM-2), finding
that larger language models generally achieve better long-form factuality.
LongFact, SAFE, and all experimental code are available at
https://github.com/google-deepmind/long-form-factuality.
SAFE, a novel method using LLMs and Google Search, evaluates long-form factual accuracy in LLM responses more cheaply and accurately than human annotators.
LLMs Large language models LongFact Search-Augmented Factuality Evaluator SAFE multi-step reasoning F1 score precision recall crowdsourced human annotators Gemini GPT Claude PaLM-2