SoulChat: Improving LLMs' Empathy, Listening, and Comfort Abilities through Fine-tuning with Multi-turn Empathy Conversations

• URL: http://arxiv.org/abs/2311.00273v1
• Date: Wed, 1 Nov 2023 03:49:52 GMT
• Title: SoulChat: Improving LLMs' Empathy, Listening, and Comfort Abilities through Fine-tuning with Multi-turn Empathy Conversations
• Authors: Yirong Chen, Xiaofen Xing, Jingkai Lin, Huimin Zheng, Zhenyu Wang, Qi Liu, Xiangmin Xu
• Abstract summary: When large language models are applied in the field of psychological counseling, they often rush to provide universal advice. We constructed a multi-turn empathetic conversation dataset of more than 2 million samples. Experiments have shown that the empathy ability of LLMs can be significantly enhanced when finetuning by using multi-turn dialogue history.
• Score: 19.11368665202549
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large language models (LLMs) have been widely applied in various fields due to their excellent capability for memorizing knowledge and chain of thought (CoT). When these language models are applied in the field of psychological counseling, they often rush to provide universal advice. However, when users seek psychological support, they need to gain empathy, trust, understanding and comfort, rather than just reasonable advice. To this end, we constructed a multi-turn empathetic conversation dataset of more than 2 million samples, in which the input is the multi-turn conversation context, and the target is empathetic responses that cover expressions such as questioning, comfort, recognition, listening, trust, emotional support, etc. Experiments have shown that the empathy ability of LLMs can be significantly enhanced when finetuning by using multi-turn dialogue history and responses that are closer to the expression of a psychological consultant.

Related papers

• APTNESS: Incorporating Appraisal Theory and Emotion Support Strategies for Empathetic Response Generation [71.26755736617478]
  Empathetic response generation is designed to comprehend the emotions of others. We develop a framework that combines retrieval augmentation and emotional support strategy integration. Our framework can enhance the empathy ability of LLMs from both cognitive and affective empathy perspectives.
  arXiv  Detail & Related papers  (2024-07-23T02:23:37Z)
• Multi-dimensional Evaluation of Empathetic Dialog Responses [4.580983642743026]
  We propose a multi-dimensional empathy evaluation framework to measure both emphexpressed intents from the speaker's perspective and emphperceived empathy from the listener's perspective. We find the two dimensions are inter-connected, while perceived empathy has high correlations with dialogue satisfaction levels.
  arXiv  Detail & Related papers  (2024-02-18T00:32:33Z)
• Sibyl: Sensible Empathetic Dialogue Generation with Visionary Commonsense Knowledge [42.93002246089028]
  We present an innovative framework named Sensible Empathetic Dialogue Generation with Visionary Commonsense Knowledge (Sibyl) Designed to concentrate on the imminent dialogue future, this paradigm directs LLMs toward the implicit requirements of the conversation. Experimental results demonstrate that incorporating our paradigm for acquiring commonsense knowledge into LLMs comprehensively enhances the quality of their responses.
  arXiv  Detail & Related papers  (2023-11-26T14:35:23Z)
• Think Before You Speak: Cultivating Communication Skills of Large Language Models via Inner Monologue [73.69510478736483]
  Large language models (LLMs) can generate fluent, coherent, and diverse responses. However, they lack a crucial ability: communication skills. This article aims to empower LLMs with communication skills through inner monologues. Experimental results show that the proposed CSIM strategy improves the backbone models and outperforms the baselines.
  arXiv  Detail & Related papers  (2023-11-13T16:19:42Z)
• Harnessing Large Language Models' Empathetic Response Generation Capabilities for Online Mental Health Counselling Support [1.9336815376402723]
  Large Language Models (LLMs) have demonstrated remarkable performance across various information-seeking and reasoning tasks. This study sought to examine LLMs' capability to generate empathetic responses in conversations that emulate those in a mental health counselling setting. We selected five LLMs: version 3.5 and version 4 of the Generative Pre-training (GPT), Vicuna FastChat-T5, Pathways Language Model (PaLM) version 2, and Falcon-7B-Instruct.
  arXiv  Detail & Related papers  (2023-10-12T03:33:06Z)
• Building Emotional Support Chatbots in the Era of LLMs [64.06811786616471]
  We introduce an innovative methodology that synthesizes human insights with the computational prowess of Large Language Models (LLMs) By utilizing the in-context learning potential of ChatGPT, we generate an ExTensible Emotional Support dialogue dataset, named ExTES. Following this, we deploy advanced tuning techniques on the LLaMA model, examining the impact of diverse training strategies, ultimately yielding an LLM meticulously optimized for emotional support interactions.
  arXiv  Detail & Related papers  (2023-08-17T10:49:18Z)
• Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning [81.79431311952656]
  We propose a novel system MultiESC to provide Emotional Support. For strategy planning, we propose lookaheads to estimate the future user feedback after using particular strategies. For user state modeling, MultiESC focuses on capturing users' subtle emotional expressions and understanding their emotion causes.
  arXiv  Detail & Related papers  (2022-10-09T12:23:47Z)
• Coral: An Approach for Conversational Agents in Mental Health Applications [0.0]
  We present an approach for creating a generative empathetic open-domain robot that can be used for mental health applications. We leverage large scale pre-training and empathetic conversational data to make the responses more empathetic in nature. Our models achieve state-of-the-art results on the Empathetic Dialogues test set.
  arXiv  Detail & Related papers  (2021-11-16T15:15:58Z)
• Exemplars-guided Empathetic Response Generation Controlled by the Elements of Human Communication [88.52901763928045]
  We propose an approach that relies on exemplars to cue the generative model on fine stylistic properties that signal empathy to the interlocutor. We empirically show that these approaches yield significant improvements in empathetic response quality in terms of both automated and human-evaluated metrics.
  arXiv  Detail & Related papers  (2021-06-22T14:02:33Z)
• Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach [10.19931220479239]
  Psychologists have repeatedly demonstrated that empathy is a key component leading to positive outcomes in supportive conversations. Recent studies have shown that highly empathic conversations are rare in online mental health platforms. We introduce a new task of empathic rewriting which aims to transform low-empathy conversational posts to higher empathy.
  arXiv  Detail & Related papers  (2021-01-19T16:37:58Z)
• Towards Persona-Based Empathetic Conversational Models [58.65492299237112]
  Empathetic conversational models have been shown to improve user satisfaction and task outcomes in numerous domains. In Psychology, persona has been shown to be highly correlated to personality, which in turn influences empathy. We propose a new task towards persona-based empathetic conversations and present the first empirical study on the impact of persona on empathetic responding.
  arXiv  Detail & Related papers  (2020-04-26T08:51:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.