Computer Science > Artificial Intelligence
arXiv:2402.06782 (cs)
[Submitted on 9 Feb 2024 (v1), last revised 25 Jul 2024 (this version, v4)]
Title:Debating with More Persuasive LLMs Leads to More Truthful Answers
Authors:Akbir Khan,John Hughes,Dan Valentine,Laura Ruis,Kshitij Sachan,Ansh Radhakrishnan,Edward Grefenstette,Samuel R. Bowman,Tim Rocktäschel,Ethan Perez
View a PDF of the paper titled Debating with More Persuasive LLMs Leads to More Truthful Answers, by Akbir Khan and 8 other authors
View PDFHTML (experimental)Abstract:Common methods for aligning large language models (LLMs) with desired behaviour heavily rely on human-labelled data. However, as models grow increasingly sophisticated, they will surpass human expertise, and the role of human evaluation will evolve into non-experts overseeing experts. In anticipation of this, we ask: can weaker models assess the correctness of stronger models? We investigate this question in an analogous setting, where stronger models (experts) possess the necessary information to answer questions and weaker models (non-experts) lack this information. The method we evaluate is debate, where two LLM experts each argue for a different answer, and a non-expert selects the answer. We find that debate consistently helps both non-expert models and humans answer questions, achieving 76% and 88% accuracy respectively (naive baselines obtain 48% and 60%). Furthermore, optimising expert debaters for persuasiveness in an unsupervised manner improves non-expert ability to identify the truth in debates. Our results provide encouraging empirical evidence for the viability of aligning models with debate in the absence of ground truth.
| Comments: | For code please check:this https URL |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2402.06782 [cs.AI] |
| (orarXiv:2402.06782v4 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2402.06782 arXiv-issued DOI via DataCite |
Submission history
From: Akbir M Khan Mr [view email][v1] Fri, 9 Feb 2024 21:05:01 UTC (7,563 KB)
[v2] Thu, 15 Feb 2024 22:09:52 UTC (7,563 KB)
[v3] Thu, 30 May 2024 13:59:34 UTC (7,579 KB)
[v4] Thu, 25 Jul 2024 23:32:21 UTC (7,579 KB)
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View a PDF of the paper titled Debating with More Persuasive LLMs Leads to More Truthful Answers, by Akbir Khan and 8 other authors
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