Benchmarking Long-Form Factuality in Large Language Models

Table of Links

Abstract and 1 Introduction

2 LongFact: Using LLMs to generate a multi-topic benchmark for long-form factuality

3 Safe:LLM agents as factuality autoraters

4 LLMs agents can be better factuality annotators than humans

5 F1@k: Extending F1 with recall from human-preferred length

6 Larger LLMs are more factual

7 Related Work

8 Limitations

9 Conclusion, Acknowledgments, Author Contribution, and References

Appendix

A. Frequently asked questions

B. LongFact details

C. SAFE details

D. Metric details

E. Further analysis

9 CONCLUSION

In this paper, we examined how to thoroughly benchmark long-form factuality in large language models. To do so, we first used GPT-4 to generate LongFact, a set of 2,280 prompts spanning 38 different topics. We then proposed using language-model agents to automatically evaluate the longform factuality of a model’s response. This method, which we call SAFE, utilizes a search-enabled large language model to split a long-form response into individual facts, revise individual facts to be self-contained, determine the relevance of each individual fact to answering the prompt, and check the factuality of each relevant fact by issuing Google Search queries. Furthermore, we extended recall into the long-form domain by utilizing a hyperparameter K as a proxy for human-preferred length of responses, and we combined this measurement of recall with precision in F1@K.

Empirically, we demonstrated that SAFE outperforms crowdsourced human annotators by agreeing with 72% of human annotations and winning 76% of examples out of a set of 100 randomly-sampled disagreement cases. We also showed that SAFE is more than 20× cheaper than crowdsourced human annotators. Moreover, we benchmarked thirteen models from four model families (Gemini, GPT, Claude, PaLM-2) on LongFact and found that larger language models generally had better long-form factuality. We make all of our code available at https://github.com/google-deepmind/ long-form-factuality.

Future research in this area can explore a wide range of directions. First, a key avenue of exploration is how to improve a language model’s long-form factuality via better pretraining/finetuning or by augmenting them with external tool use. There also exists areas of improvement for SAFE in terms of reliance on search-enabled language-model agents (we discuss this further in Appendix C.6). Furthermore, our work considers factuality (i.e., correctness of facts with respect to world knowledge), and so it is still unclear how to reliably measure hallucination (i.e., correctness of facts with respect to a model’s internal knowledge) in long-form settings.

Through our benchmark, we aim to have demonstrated how reliable methods of obtaining datasets, evaluating models, and aggregating metrics can significantly improve our understanding of model capabilities in long-form settings. We hope that our work encourages further research into both measuring and improving language models in long-form domains.

ACKNOWLEDGEMENTS

We thank Chen Liang for suggestions on improving LongFact. We thank Balaji Lakshminarayanan, Jeremiah Liu, Clara Huiyi Hu, Kelvin Guu, Ice Pasupat, Zhuyun Dai, Harshal Godhia, Vered Cohen, Eran Ofek, Chun-Sung Ferng, Da-Cheng Juan, Hao Zhou, Chenjie Gu, Qijun Tan, Jasper Snoek, Daniel Balle, Petru Gurita, Zhichun Wu, Norbert Kalb, Yanyan Zheng, Qiuchen Guo, John Nham, and Fangtao Li for helpful discussion on factuality autoraters. We thank Albert Webson, Adams Yu, Chen Zhu, Heng-Tze Cheng, Chenkai Kuang, YaGuang Li, and Xinyun Chen for insightful feedback on our initial ideas. We thank Denny Zhou, Slav Petrov, Le Hou, and Andrew Dai for providing feedback on the paper.

AUTHOR CONTRIBUTIONS

Chengrun Yang and Jerry Wei proposed framing the paper in the long-form setting. Jerry Wei prototyped using an LLM to automatically generate prompts, and Chengrun Yang proposed and prototyped generating topic-specific prompts for LongFact. Xinying Song, Jie Huang, Cosmo Du, and Chengrun Yang came up with the idea of developing a search-augmented LLM autorater, which was prototyped by Chengrun Yang, Jerry Wei, Yifeng Lu, Xinying Song, Jie Huang, and Cosmo Du. Yifeng Lu prototyped a multi-step agent approach using LLMs. Jerry Wei and Xinying Song implemented the current system of SAFE. Jerry Wei proposed names for LongFact and SAFE, and Nathan Hu proposed the name for F1@K. Nathan Hu proposed to incorporate human-expected length into the recall for F1@K.

Jerry Wei led implementation and experimentation that resulted in the current codebase. Chengrun Yang, Yifeng Lu, Xinying Song, Nathan Hu, and Quoc V. Le helped develop ideas for experimentation. Chengrun Yang contributed to the codebase and experiments and examined experiment data, which influenced technical direction. Jerry Wei led open-sourcing efforts with help from Da Huang, Dustin Tran, and Chengrun Yang. Jerry Wei, Chengrun Yang, and Nathan Hu led paper writing. Quoc V. Le and Yifeng Lu advised the project. Quoc V. Le proposed the direction to work on factuality and hallucination. All other authors helped contribute feedback on paper framing.

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