前沿大语言模型 Asch 从众效应的定量研究方法
A Quantitative Approach to Asch Conformity in Frontier Large Language Models
AI 导读
一项 arXiv 研究对前沿大语言模型的 Asch 从众效应进行了定量分析。与传统在多问题上采样不同，该方法对单一问题重复采样，比较模型接触人为制造的共识前后答案分布的差异。实验证实前沿 LLM 确实会从众，并提供了导致从众的条件及程度的定量数据。
正文 · 原文
Abstract:The Asch conformity experiment has recently gained considerable attention as a way of measuring conformity in large language models (LLMs). While previous studies have focused on demonstrating conformity in LLMs, our work adds a further quantitative layer to this approach. Instead of sampling a frontier LLM on a suite of questions, our approach samples a single question repeatedly, leveraging the probabilistic nature of LLMs to produce a distribution of answers. In doing so, we can compare the distributions of answers before and after the model is presented with the manufactured consensus. Our experiments confirm that frontier LLMs do conform, reinforcing prior literature, and, through our distribution-to-distribution comparisons, provide additional quantitative data on the conditions that lead to conformity and the degree to which it occurs.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.36668 [cs.CL] |
| (or arXiv:2610.36668v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.36668 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Stylianos Loukas Vasileiou [view email]
[v1]
Tue, 27 Oct 2026 05:40:08 UTC (133 KB)
来源：arXiv cs.AI · arxiv.org