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        <title>LM vs LM: Detecting Factual Errors via Cross Examination</title>
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    <abstract>A prominent weakness of modern language models (LMs) is their tendency to generate factually incorrect text, which hinders their usability. A natural question is whether such factual errors can be detected automatically. Inspired by truth-seeking mechanisms in law, we propose a factuality evaluation framework for LMs that is based on cross-examination. Our key idea is that an incorrect claim is likely to result in inconsistency with other claims that the model generates. To discover such inconsistencies, we facilitate a multi-turn interaction between the LM that generated the claim and another LM (acting as an examiner) which introduces questions to discover inconsistencies. We empirically evaluate our method on factual claims made by multiple recent LMs on four benchmarks, finding that it outperforms existing methods and baselines, often by a large gap. Our results demonstrate the potential of using interacting LMs for capturing factual errors.</abstract>
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%0 Conference Proceedings
%T LM vs LM: Detecting Factual Errors via Cross Examination
%A Cohen, Roi
%A Hamri, May
%A Geva, Mor
%A Globerson, Amir
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F cohen-etal-2023-lm
%X A prominent weakness of modern language models (LMs) is their tendency to generate factually incorrect text, which hinders their usability. A natural question is whether such factual errors can be detected automatically. Inspired by truth-seeking mechanisms in law, we propose a factuality evaluation framework for LMs that is based on cross-examination. Our key idea is that an incorrect claim is likely to result in inconsistency with other claims that the model generates. To discover such inconsistencies, we facilitate a multi-turn interaction between the LM that generated the claim and another LM (acting as an examiner) which introduces questions to discover inconsistencies. We empirically evaluate our method on factual claims made by multiple recent LMs on four benchmarks, finding that it outperforms existing methods and baselines, often by a large gap. Our results demonstrate the potential of using interacting LMs for capturing factual errors.
%R 10.18653/v1/2023.emnlp-main.778
%U https://aclanthology.org/2023.emnlp-main.778
%U https://doi.org/10.18653/v1/2023.emnlp-main.778
%P 12621-12640

Markdown (Informal)

[LM vs LM: Detecting Factual Errors via Cross Examination](https://aclanthology.org/2023.emnlp-main.778) (Cohen et al., EMNLP 2023)

• LM vs LM: Detecting Factual Errors via Cross Examination (Cohen et al., EMNLP 2023)

ACL

• Roi Cohen, May Hamri, Mor Geva, and Amir Globerson. 2023. LM vs LM: Detecting Factual Errors via Cross Examination. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 12621–12640, Singapore. Association for Computational Linguistics.