Can Large Language Models be Good Emotional Supporter? Mitigating
  Preference Bias on Emotional Support Conversation
Dongjin Kang Sunghwan Kim Taeyoon Kwon Seungjun Moon Hyunsouk Cho Youngjae Yu Dongha Lee Jinyoung Yeo
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) LLMs alone cannot become good
emotional supporters. These insights suggest promising avenues for future
research to enhance the emotional intelligence of LLMs.