LLM不反驳有害信念，因默认迎合用户且缺乏批判性警惕。
Accommodation and Epistemic Vigilance: A Pragmatic Account of Why LLMs Fail to Challenge Harmful Beliefs
- 通过语用学视角分析模型为何迎合用户假设
- 添加'等一下'可显著提升挑战有害观点的能力
- 适合关注AI安全与对话伦理的研究者参考
大型语言模型（LLMs）在医疗建议到社会推理等多个领域，常无法反驳用户持有的有害信念。我们认为，这种失败可从语用学角度理解为模型默认迎合用户假设并缺乏足够的认知警惕性所致。我们发现，人类中影响顺从性的社会与语言因素——议题相关性、语言编码方式、信息源可信度——同样影响LLMs的顺从行为，解释了三个安全基准测试中的性能差异：涵盖错误信息（Cancer-Myth、SAGE-Eval）和阿谀奉承（ELEPHANT）。进一步研究表明，仅通过添加短语‘wait a minute’等简单语用干预，即可显著提升模型在这些基准上的表现，同时保持低误报率。结果强调，在评估与提升大模型行为安全性时，必须考虑语用机制。
原文摘要 · Abstract (English)
Large language models (LLMs) frequently fail to challenge users' harmful beliefs in domains ranging from medical advice to social reasoning. We argue that these failures can be understood and addressed pragmatically as consequences of LLMs defaulting to accommodating users' assumptions and exhibiting insufficient epistemic vigilance. We show that social and linguistic factors known to influence accommodation in humans (at-issueness, linguistic encoding, and source reliability) similarly affect accommodation in LLMs, explaining performance differences across three safety benchmarks that test models' ability to challenge harmful beliefs, spanning misinformation (Cancer-Myth, SAGE-Eval) and sycophancy (ELEPHANT). We further show that simple pragmatic interventions, such as adding the phrase "wait a minute", significantly improve performance on these benchmarks while preserving low false-positive rates. Our results highlight the importance of considering pragmatics for evaluating LLM behavior and improving LLM safety.
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