面向情感一致性分析的大型语言模型在情感对话情境中的研究
Towards Emotion Consistency Analysis of Large Language Models in Emotional Conversational Contexts
AI总结：
本文研究了大型语言模型在情感对话中生成响应的一致性，通过极端和中等情绪的三个查询测试，发现模型在处理包含错误假设的查询时表现欠佳，尤其在中等情绪内容中更易受虚假信念影响。
摘要：
本文对大型语言模型在情感驱动对话情境中生成响应的一致性进行了分析。具体而言，将LLM生成的文本作为查询提交给同一模型，并评估其响应。通过三个查询，分别涉及不同强度的错误假设（虚假前提），测试了两个商业模型（Claude-3.5-haiku、GPT4o-mini）和一个中型模型（Mistral-7B）。研究发现，LLM在处理包含错误信念的查询时表现低于平均水平，且在中等情绪内容中尤为敏感。扩展的注意力评分分析显示，模型的优先级从评估转向生成。这些结果对LLM在高风险、情感敏感情境中的应用提出了重要考虑。
英文摘要：
In this work, we conduct an analysis to examine the consistency of Large Language Models (LLMs) with respect to their own generated responses in an emotionally-driven conversational context. Specifically, the text generated by LLM is framed as a query to the same model, and its responses are subsequently assessed. This is performed with three queries across two dimensions of extreme and moderate emotions. The three queries are, in particular, false claim queries that contain inherently wrong assumptions (false presuppositions) in increasing order of intensity. Two commercial models, Claude-3.5-haiku, GPT4o-mini, and a medium-sized model, Mistral-7B, are considered in the study. Our findings indicate that LLMs exhibit below-average performance and remain vulnerable to false beliefs embedded within queries. This susceptibility is especially pronounced for moderate emotional content. Furthermore, an extended attention-score-based analysis highlights a shift in models' priority from evaluative to generative. The results raise important considerations for LLMs' deployment in high-stakes, emotionally sensitive contexts.
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