Long-form factuality in large language models
Authors: name: Jerry Wei, name: Chengrun Yang, name: Xinying Song, name: Yifeng Lu, name: Nathan Hu, name: Dustin Tran, name: Daiyi Peng, name: Ruibo Liu, name: Da Huang, name: Cosmo Du, name: Quoc V. Le

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.