大型语言模型能否成为好的情感支持者？缓解情感支持对话中的偏好偏差
Can Large Language Models be Good Emotional Supporter? Mitigating Preference Bias on Emotional Support Conversation
AI总结：
本研究分析LLMs在情感支持对话中的策略选择偏好，发现高偏好阻碍有效支持，提出外部辅助可缓解偏差，并指出现有LLMs难以独立成为好的情感支持者。
摘要：
情感支持对话（ESC）是一项旨在通过日常对话缓解个体情感困扰的任务。鉴于其固有的复杂性和非直观性，ESConv数据集引入了支持策略，以促进生成恰当的回应。近期，尽管大型语言模型（LLMs）展现出卓越的对话能力，但先前研究表明，它们在提供有效情感支持方面常常存在困难。因此，本研究首先分析了LLMs在ESConv上的表现，揭示了其在选择正确策略方面的挑战，以及对特定策略的显著偏好。受此启发，我们探讨了LLMs内在偏好对提供情感支持的影响，并观察到，对特定策略表现出高偏好会阻碍有效的情感支持，削弱其在预测恰当策略时的鲁棒性。此外，我们开展了一项方法论研究，以深入理解LLMs成为熟练情感支持者所需的必要方法。我们的发现强调：（1）对特定策略的低偏好会阻碍情感支持的进展，（2）外部辅助有助于减少偏好偏差，（3）现有LLMs单独无法成为好的情感支持者。这些见解为未来研究提升LLMs的情感智能提供了有前景的方向。
英文摘要：
Emotional Support Conversation (ESC) is a task aimed at alleviating individuals' emotional distress through daily conversation. Given its inherent complexity and non-intuitive nature, ESConv dataset incorporates support strategies to facilitate the generation of appropriate responses. Recently, despite the remarkable conversational ability of large language models (LLMs), previous studies have suggested that they often struggle with providing useful emotional support. Hence, this work initially analyzes the results of LLMs on ESConv, revealing challenges in selecting the correct strategy and a notable preference for a specific strategy. Motivated by these, we explore the impact of the inherent preference in LLMs on providing emotional support, and consequently, we observe that exhibiting high preference for specific strategies hinders effective emotional support, aggravating its robustness in predicting the appropriate strategy. Moreover, we conduct a methodological study to offer insights into the necessary approaches for LLMs to serve as proficient emotional supporters. Our findings emphasize that (1) low preference for specific strategies hinders the progress of emotional support, (2) external assistance helps reduce preference bias, and (3) existing LLMs alone cannot become good emotional supporters. These insights suggest promising avenues for future research to enhance the emotional intelligence of LLMs.
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