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.

情感支持对话（ESC）是通过日常对话来缓解个人情感困扰的任务。本研究分析了大型语言模型（LLMs）在 ESConv 上的结果，揭示了挑选正确策略的挑战和对特定策略的显著偏好。通过对 LLMs 内在偏好对情感支持的影响进行探索，发现对特定策略的高偏好阻碍了有效的情感支持，导致其在预测适当策略方面的鲁棒性进一步加剧。此外，我们进行了一项方法论研究，为 LLMs 成为优秀情感支持者提供了见解。研究结果强调了（1）对特定策略的低偏好阻碍了情感支持的进展，（2）外部辅助有助于减少偏好偏差，以及（3）单靠 LLMs 无法成为优秀情感支持者。这些发现为增强 LLMs 的情感智能提供了有前景的研究方向。

大型语言模型能成为良好的情感支持者吗？在情感支持对话中缓解偏好偏差

Can Large Language Models be Good Emotional Supporter? Mitigating  Preference Bias on Emotional Support Conversation

Emotional support is a crucial ability for many conversation scenarios,
including social interactions, mental health support, and customer service
chats. Following reasonable procedures and using various support skills can
help to effectively provide support. However, due to the lack of a
well-designed task and corpora of effective emotional support conversations,
research on building emotional support into dialog systems remains untouched.
In this paper, we define the Emotional Support Conversation (ESC) task and
propose an ESC Framework, which is grounded on the Helping Skills Theory. We
construct an Emotion Support Conversation dataset (ESConv) with rich annotation
(especially support strategy) in a help-seeker and supporter mode. To ensure a
corpus of high-quality conversations that provide examples of effective
emotional support, we take extensive effort to design training tutorials for
supporters and several mechanisms for quality control during data collection.
Finally, we evaluate state-of-the-art dialog models with respect to the ability
to provide emotional support. Our results show the importance of support
strategies in providing effective emotional support and the utility of ESConv
in training more emotional support systems.

本研究旨在探讨如何在对话系统中建立情感支持，并构建了基于 “Helping Skills Theory” 的 “Emotional Support Conversation（ESC）” 任务和 ESC 框架。同时设计了一个注释丰富的情感支持对话数据集（ESConv），并对该数据集进行了质量控制及训练，最后通过与最新的对话模型进行评估，证明了支持策略对于提供有效情感支持的重要性以及 ESConv 在训练更多情感支持系统方面的实用性。