大语言模型作为调解者：它们能准确诊断冲突吗？
LLMs as mediators: Can they diagnose conflicts accurately?
AI总结：
本文研究了大语言模型在诊断冲突根源方面的准确性，发现GPT 4在特定条件下对因果分歧的判断存在偏差，而GPT 3.5表现较弱。
摘要：
先前的研究表明，要能够调解冲突，观察者必须能够可靠地区分分歧的来源，这些分歧源于对什么是真实的信念差异（因果性）与对价值观的差异（道德性）。在本文中，我们测试了OpenAI的大型语言模型GPT 3.5和GPT 4是否能够执行此任务，并确定哪一种分歧对LLM诊断尤为具有挑战性。我们复制了Koçak等人（2003）的研究，该研究采用了一个情景设计，使用OpenAI的GPT 3.5和GPT 4。我们发现，这两个LLM在因果性和道德性区分上的语义理解与人类相似，并且能够可靠地区分它们。当被要求诊断对话中的分歧来源时，两个LLM相比人类表现出倾向于高估道德不一致条件中因果分歧的范围，低估道德分歧的范围的倾向。这种倾向在使用依赖于具体问题语言的近似尺度时，特别是在GPT 4中尤为明显。当使用近似或远距离尺度时，GPT 3.5的表现不如GPT 4或人类。本研究为使用LLM调解冲突提供了首次测试，通过诊断分歧的根源在因果性和评估性代码中潜在的可能性。
英文摘要：
Prior research indicates that to be able to mediate conflict, observers of disagreements between parties must be able to reliably distinguish the sources of their disagreement as stemming from differences in beliefs about what is true (causality) vs. differences in what they value (morality). In this paper, we test if OpenAI's Large Language Models GPT 3.5 and GPT 4 can perform this task and whether one or other type of disagreement proves particularly challenging for LLM's to diagnose. We replicate study 1 in Koçak et al. (2003), which employes a vignette design, with OpenAI's GPT 3.5 and GPT 4. We find that both LLMs have similar semantic understanding of the distinction between causal and moral codes as humans and can reliably distinguish between them. When asked to diagnose the source of disagreement in a conversation, both LLMs, compared to humans, exhibit a tendency to overestimate the extent of causal disagreement and underestimate the extent of moral disagreement in the moral misalignment condition. This tendency is especially pronounced for GPT 4 when using a proximate scale that relies on concrete language specific to an issue. GPT 3.5 does not perform as well as GPT4 or humans when using either the proximate or the distal scale. The study provides a first test of the potential for using LLMs to mediate conflict by diagnosing the root of disagreements in causal and evaluative codes.
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。
