Summary of Debating with More Persuasive Llms Leads to More Truthful Answers, by Akbir Khan et al.
Debating with More Persuasive LLMs Leads to More Truthful Answers
by Akbir Khan, John Hughes, Dan Valentine, Laura Ruis, Kshitij Sachan, Ansh Radhakrishnan, Edward Grefenstette, Samuel R. Bowman, Tim Rocktäschel, Ethan Perez
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers explore the possibility of weaker language models assessing the correctness of stronger models. They propose a method called “debate” where two experts argue for different answers and a non-expert selects the correct one. The results show that debate helps both humans and non-expert models achieve higher accuracy, with 76% and 88% respectively. Furthermore, optimizing expert debaters improves non-expert ability to identify truth in debates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows that even weaker language models can help identify correct answers by having experts debate each other. The method called “debate” is simple yet effective, achieving high accuracy for both humans and non-expert models. This could be an important step towards aligning large language models with desired behavior in the future. |
