适应与认知警觉：一种实用视角下为何LLMs未能挑战有害信念
Accommodation and Epistemic Vigilance: A Pragmatic Account of Why LLMs Fail to Challenge Harmful Beliefs
AI总结：
本文通过语用学视角揭示LLMs在挑战有害信念时的失败原因，并提出简单干预提升安全性的方法。
摘要：
大型语言模型（LLMs）在从医疗建议到社会推理的各个领域中，经常未能挑战用户的有害信念。我们主张，这些失败可以被理解并解决为语用学上的结果，即LLMs默认适应用户假设并表现出不足的认知警觉。我们显示，已知影响人类适应的社会和语言因素（在场性、语言编码和来源可靠性）同样影响LLMs的适应，解释了在三个安全基准测试中模型挑战有害信念能力的差异，涵盖虚假信息（癌症谬误、SAGE-Eval）和阿谀（ELEPHANT）。我们进一步显示，简单的语用干预，如添加“等等”这句话，显著提高了这些基准测试的表现，同时保持低的误报率。我们的结果强调了在评估LLM行为和改进LLM安全时考虑语用学的重要性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
