Summary of Conformity in Large Language Models, by Xiaochen Zhu and Caiqi Zhang and Tom Stafford and Nigel Collier and Andreas Vlachos
Conformity in Large Language Models
by Xiaochen Zhu, Caiqi Zhang, Tom Stafford, Nigel Collier, Andreas Vlachos
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This study investigates the conformity effect in large language models (LLMs), which are increasingly used as conversation partners to improve productivity. The researchers adapt psychological experiments to examine how LLMs align their responses with the majority, finding that all tested models exhibit varying levels of conformity, regardless of correctness or initial choice. They also identify factors that influence conformity, such as training paradigms and input characteristics. To mitigate this bias, the authors propose two interventions: Devil’s Advocate and Question Distillation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how large language models (LLMs) are influenced by others’ opinions. It shows that LLMs tend to agree with majority responses, even if they’re wrong. The study finds that some factors make this conformity worse or better. To fix this problem, the researchers suggest two new methods: Devil’s Advocate and Question Distillation. |
Keywords
» Artificial intelligence » Distillation
