SweetieChat: A Strategy-Enhanced Role-playing Framework for Diverse Scenarios Handling Emotional Support Agent

• URL: http://arxiv.org/abs/2412.08389v1
• Date: Wed, 11 Dec 2024 13:56:04 GMT
• Title: SweetieChat: A Strategy-Enhanced Role-playing Framework for Diverse Scenarios Handling Emotional Support Agent
• Authors: Jing Ye, Lu Xiang, Yaping Zhang, Chengqing Zong,
• Abstract summary: Large Language Models (LLMs) have demonstrated promising potential in providing empathetic support during interactions.<n>We propose an innovative strategy-enhanced role-playing framework, designed to simulate authentic emotional support conversations.<n>Within this framework, we develop the textbfServeForEmo dataset, comprising an extensive collection of 3.7K+ multi-turn dialogues and 62.8K+ utterances.
• Score: 27.301608019492043
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have demonstrated promising potential in providing empathetic support during interactions. However, their responses often become verbose or overly formulaic, failing to adequately address the diverse emotional support needs of real-world scenarios. To tackle this challenge, we propose an innovative strategy-enhanced role-playing framework, designed to simulate authentic emotional support conversations. Specifically, our approach unfolds in two steps: (1) Strategy-Enhanced Role-Playing Interactions, which involve three pivotal roles -- Seeker, Strategy Counselor, and Supporter -- engaging in diverse scenarios to emulate real-world interactions and promote a broader range of dialogues; and (2) Emotional Support Agent Training, achieved through fine-tuning LLMs using our specially constructed dataset. Within this framework, we develop the \textbf{ServeForEmo} dataset, comprising an extensive collection of 3.7K+ multi-turn dialogues and 62.8K+ utterances. We further present \textbf{SweetieChat}, an emotional support agent capable of handling diverse open-domain scenarios. Extensive experiments and human evaluations confirm the framework's effectiveness in enhancing emotional support, highlighting its unique ability to provide more nuanced and tailored assistance.

Related papers

• From Personas to Talks: Revisiting the Impact of Personas on LLM-Synthesized Emotional Support Conversations [19.67703146838264]
  Large Language Models (LLMs) have revolutionized the generation of emotional support conversations. This paper explores the role of personas in the creation of emotional support conversations.
  arXiv  Detail & Related papers  (2025-02-17T05:24:30Z)
• A Survey on Multi-Turn Interaction Capabilities of Large Language Models [47.05742294162551]
  Multi-turn interaction in the dialogue system research refers to a system's ability to maintain context across multiple dialogue turns. Recent advancements in large language models (LLMs) have significantly expanded the scope of multi-turn interaction.
  arXiv  Detail & Related papers  (2025-01-17T05:21:49Z)
• PanoSent: A Panoptic Sextuple Extraction Benchmark for Multimodal Conversational Aspect-based Sentiment Analysis [74.41260927676747]
  This paper bridges the gaps by introducing a multimodal conversational Sentiment Analysis (ABSA) To benchmark the tasks, we construct PanoSent, a dataset annotated both manually and automatically, featuring high quality, large scale, multimodality, multilingualism, multi-scenarios, and covering both implicit and explicit sentiment elements. To effectively address the tasks, we devise a novel Chain-of-Sentiment reasoning framework, together with a novel multimodal large language model (namely Sentica) and a paraphrase-based verification mechanism.
  arXiv  Detail & Related papers  (2024-08-18T13:51:01Z)
• Steering Conversational Large Language Models for Long Emotional Support Conversations [4.984018914962973]
  We focus on the steerability of the Llama-2 and Llama-3 suite of models, examining their ability to maintain these strategies throughout interactions. To assess this, we introduce the Strategy Relevant Attention (SRA) metric, which quantifies the model's adherence to the prompted strategy through attention maps.
  arXiv  Detail & Related papers  (2024-02-16T05:03:01Z)
• STICKERCONV: Generating Multimodal Empathetic Responses from Scratch [23.733723334721695]
  We introduce the Agent for STICKERCONV (Agent4SC), which uses collaborative agent interactions to realistically simulate human behavior with sticker usage. We develop a multimodal empathetic dialogue dataset, STICKERCONV, comprising 12.9K dialogue sessions, 5.8K unique stickers, and 2K diverse conversational scenarios. To advance further, we propose PErceive and Generate Stickers (PEGS), a multimodal empathetic response generation framework.
  arXiv  Detail & Related papers  (2024-01-20T13:44:21Z)
• Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation [52.930183136111864]
  We propose using scorable negotiation to evaluate Large Language Models (LLMs) To reach an agreement, agents must have strong arithmetic, inference, exploration, and planning capabilities. We provide procedures to create new games and increase games' difficulty to have an evolving benchmark.
  arXiv  Detail & Related papers  (2023-09-29T13:33:06Z)
• 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)
• Facilitating Multi-turn Emotional Support Conversation with Positive Emotion Elicitation: A Reinforcement Learning Approach [58.88422314998018]
  Emotional support conversation (ESC) aims to provide emotional support (ES) to improve one's mental state. Existing works stay at fitting grounded responses and responding strategies which ignore the effect on ES and lack explicit goals to guide emotional positive transition. We introduce a new paradigm to formalize multi-turn ESC as a process of positive emotion elicitation.
  arXiv  Detail & Related papers  (2023-07-16T09:58:44Z)
• 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)
• Towards Emotional Support Dialog Systems [61.58828606097423]
  We define the Emotional Support Conversation 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. We evaluate state-of-the-art dialog models with respect to the ability to provide emotional support.
  arXiv  Detail & Related papers  (2021-06-02T13:30:43Z)

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.