ESC -Eval: Evaluating Emotion Support Conversations in Large Language Models (2024)

Abstract: Emotion Support Conversation (ESC) is a crucial application, which aims to reduce human stress, offer emotional guidance, and ultimately enhance human mental and physical well-being. With the advancement of Large Language Models (LLMs), many researchers have employed LLMs as the ESC models. However, the evaluation of these LLM-based ESCs remains uncertain. In detail, we first re-organize 2,801 role-playing cards from seven existing datasets to define the roles of the role-playing agent. Second, we train a specific role-playing model called ESC-Role which behaves more like a confused person than GPT-4. Third, through ESC-Role and organized role cards, we systematically conduct experiments using 14 LLMs as the ESC models, including general AI-assistant LLMs (e.g., ChatGPT) and ESC-oriented LLMs (e.g., ExTES-Llama). We conduct comprehensive human annotations on interactive multi-turn dialogues of different ESC models. The results show that ESC-oriented LLMs exhibit superior ESC abilities compared to general AI-assistant LLMs, but there is still a gap behind human performance. Moreover, to automate the scoring process for future ESC models, we developed ESC-RANK, which trained on the annotated data, achieving a scoring performance surpassing 35 points of GPT-4.

## introduction with the rapid development of large language models (llms), an increasing number of individu-als are engaging in conversations with llms (e.g., chatgpt @xcite ). among various conversational applications, emotional support conversation @xcite (esc) stands out as a particularly promising field, where people can freely share their personal experiences or concerns, receiving emotional support and practical advice. this interaction helps alleviate human pressures @xcite , thereby improving overall well-being. recently, numerous llm-based esc models have received wide research attention @xcite @xcite . however, effective and comprehensive evaluation of these chatbots remains challenging. current esc evaluation @xcite generally uses text-based statistical metrics or manual evaluations. (1) when using text-based statistical metrics, researchers provide the dialogue history to the esc models and then use the models to generate the corresponding responses (c.f., left panel in figure 1 ). based on the generated responses, text-based statistical metrics (such as rouge @xcite and bleu @xcite ) assess whether the responses resemble ground truth. however, these metrics heavily rely on ground truth responses, which lack objectivity @xcite due to the complex nature of esc. furthermore, since the conversation history from ground truth is provided to the model under evaluation, text-based statistical metrics cannot fully assess models' capabilities in multi-turn esc dialogues due to none self-generated bias. to alleviate the above issues, we propose esceval, which replaces human labor with role-playing llms (right panel in figure 1 ) to achieve efficient and comprehensive esc evaluation. we assign role-playing llm engaging in multi-turn conversations with esc chatbots under evaluation and collect the conversation data as the target of evaluation. in this manner, esc-eval is expected to efficiently achieve performance comparable to human evaluation that involves naturalistic multi-turn dialogues data, while getting rid of reliance on ground truth and heavy labor requirements. however, to ensure the effectiveness of our evaluation framework, two components are important: i) diverse role cards sourced from a variety of troubled individuals in real-world scenarios, which could be used to guide the llm role-playing during evaluation and ensure the comprehensive evaluation. ii) a role-playing agent that closely mirrors real human behavior, enabling the acquisition of data that faithfully reflects real human interactions, thereby guaranteeing the objectivity and fairness of the evaluation results. to accomplish these two objectives, firstly, we propose to reconstruct role cards from seven existing qa and dialogue datasets @xcite @xcite @xcite @xcite , which are relevant to emotional companionship or psychological counseling. however, these datasets do not contain user cards, thus, we use gpt-4 to extract and summarize the key information of users followed by a two-stage filtering process involving gpt-4 and human judgment. in this manner, we obtain 2,801 qualified role cards. secondly, to construct reliable role-playing agents, we propose to develop a role-playing agent for esc-eval. in detail, we construct a dataset consisting of 3.5k esc role-playing data from esconv @xcite , @xcite and @xcite , each data appeared in the format of a role card and multi-turn dialogue. we also enrich the data up to 14k by incorporating five existing role-playing instruction datasets. through fine-tuning qwen1.5 @xcite , we develop a role-playing model called esc-role. compared with existing state-of-the-art role-playing models, like @xcite , the esc-role behaves more like a person encountering real-life troubles. with the completion of esc-eval, considering huge amounts of human annotations, we select 655 high-quality role cards and comprehensively evaluate 14 llms with esc-role, including general aiassistant llms (e.g., @xcite ), and esc-oriented llms (e.g., @xcite ). after obtaining 8.5k interactive dialogues based on the 14 llms, we conduct comprehensive human evaluations and collect 59,654 manual evaluation results in terms of 7 dimensions (i.e., fluency, diversity, empathy, information, humanoid, skillful, and overall). the evaluation results show that the esc-oriented llms outperform most general aiassistant llms, but get poor performance on emotion support knowledge and human preferences. finally, to automate the scoring process for future esc models, we train esc-rank using the 59,654 manual evaluation results, achieving a scoring performance that surpasses gpt-4 by 35 points in terms of accuracy. our main contributions are concluded as follows: • we propose esc-eval, the first framework for evaluating llm-based esc models via roleplaying. it features 2801 diverse user cards with fine-grained information, a dedicated roleplaying model closely resembling individuals experiencing distress, and 7 meticulously designed dimensions for rigorous evaluation. ## evaluation in this section, we conduct evaluations on 14 general llms and domain-specific llms on esc-eval. we first introduce the models for evaluation. then we display our experimental results. finally, we display the details of our scoring model esc-rank. ## conclusion this paper proposes a novel approach to evaluate the effectiveness and sustainability of the emotion support conversation (esc) in large language models (llms) by utilizing a role-playing model to acquire multi-turn dialogue data. experimental results demonstrate the efficacy and viability of our proposed method. our evaluation outcomes indicate that while some esc models currently outperform general models, there is still significant room for improvement in terms of these models' knowledge capabilities and human-preference abilities. we encourage researchers to participate in esc research and contribute to the development of more robust esc models.