Computer Science > Computation and Language
arXiv:2305.13281 (cs)
[Submitted on 22 May 2023]
Title:LM vs LM: Detecting Factual Errors via Cross Examination
View a PDF of the paper titled LM vs LM: Detecting Factual Errors via Cross Examination, by Roi Cohen and 3 other authors
View PDFAbstract: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.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2305.13281 [cs.CL] |
| (orarXiv:2305.13281v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2305.13281 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled LM vs LM: Detecting Factual Errors via Cross Examination, by Roi Cohen and 3 other authors
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