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Can Large Language Models be Good Emotional Supporter? Mitigating Preference Bias on Emotional Support Conversation
  • Kang, Dongjin ;
  • Kim, Sunghwan ;
  • Kwon, Taeyoon ;
  • Moon, Seungjun ;
  • Cho, Hyunsouk ;
  • Yu, Youngjae ;
  • Lee, Dongha ;
  • Yeo, Jinyoung
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Publication Year
2024-01-01
Journal
Proceedings of the Annual Meeting of the Association for Computational Linguistics
Publisher
Association for Computational Linguistics (ACL)
Citation
Proceedings of the Annual Meeting of the Association for Computational Linguistics, Vol.1, pp.15232-15261
Mesh Keyword
Correct strategyEmotional intelligenceEmotional supportsInherent complexityLanguage modelMethodological studies
All Science Classification Codes (ASJC)
Computer Science ApplicationsLinguistics and LanguageLanguage and Linguistics
Abstract
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.
ISSN
0736-587X
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/37107
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85204495341&origin=inward
Journal URL
https://aclweb.org/
Type
Conference
Funding
This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT)(No.RS-2020-II201361, Artificial Intelligence Graduate School Program (Yonsei University)) and (No.RS-2021-II212068, Artificial Intelligence Innovation Hub) and (No.RS-2022-II220077,AI Technology Development for Commonsense Extraction, Reasoning, and Inference from Heterogeneous Data). Jinyoung Yeo is a corresponding author.
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