Flexible Thinking for Multimodal Emotional Support Conversation via Reinforcement Learning (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Current Chain-of-Thought based ESC methods often employ rigid, text-only reasoning, limiting adaptability in dynamic, multimodal interactions and introducing reasoning noise that degrades support quality. |
| Approach: | They propose a framework that integrates supervised fine-tuning with reinforcement learning to improve ESC models' response quality. |
| Outcome: | The proposed framework enables models to select contextually relevant thinking aspects: Visual Scene, Emotion, Situation, and Response Strategy. |
Similar Papers
Facilitating Multi-turn Emotional Support Conversation with Positive Emotion Elicitation: A Reinforcement Learning Approach (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to provide emotional support (ESC) ignore the effect on ES and lack explicit goals to guide emotional positive transition. |
| Approach: | They propose a new paradigm to formalize multi-turn ESC as a process of positive emotion elicitation. |
| Outcome: | The proposed model outperforms existing models in achieving positive emotion elicitation while maintaining conversational goals like coherence. |
Causal-ESC: Reliable Policy Learning for Emotional Support Conversation via Causal Inference (2026.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to Emotional Support Conversation (ESC) are mechanistically opaque and lacks a causal mechanism between dialogue features and effective empathic strategies. |
| Approach: | They propose a framework that uses Doubly Robust learning to model causal effects of utterance features on strategy selection. |
| Outcome: | The proposed framework outperforms state-of-the-art baselines in empathy and helpfulness and provides a theoretically grounded, interpretable solution to the mechanistic interpretability dilemma in affective computing. |
MultiAgentESC: A LLM-based Multi-Agent Collaboration Framework for Emotional Support Conversation (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies focus on generating responses directly and neglect integration of domain-specific reasoning and expert interaction. |
| Approach: | They propose a training-free multi-agent collaboration framework for ESC to emulate human-like process of providing emotional support through dialogue analysis, strategy deliberation, and response generation. |
| Outcome: | The proposed framework excels at providing emotional support and diversifying support strategy selection. |
Convert Language Model into a Value-based Strategic Planner (2025.acl-industry)
Copied to clipboard
| Challenge: | Emotional support conversation (ESC) aims to alleviate the emotional distress of individuals through effective conversations. |
| Approach: | They propose a framework that bootstraps the planning during ESC and determines the optimal strategy based on long-term returns. |
| Outcome: | The proposed framework outperforms baseline models on ESC datasets and can be used to guide the LLM to response. |
Chain of Strategy Optimization Makes Large Language Models Better Emotional Supporter (2025.findings-emnlp)
Copied to clipboard
Weixiang Zhao, Xingyu Sui, Xinyang Han, Yang Deng, Yulin Hu, Jiahe Guo, Libo Qin, Qianyun Du, Shijin Wang, Yanyan Zhao, Bing Qin, Ting Liu
| Challenge: | Existing supervised fine-tuning (SFT) fails to address these issues, as it trains models on single gold-standard responses without modeling nuanced strategy trade-offs. |
| Approach: | They propose a two-stage framework that optimizes strategy selection preferences at each dialogue turn. |
| Outcome: | The proposed framework improves strategy selection preferences at each dialogue turn. |
Beyond Coarse Labels: Fine-Grained Problem Augmentation and Multi-Dimensional Feedback for Emotional Support Conversation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing ESC datasets often use coarse-grained problem categories, limiting models’ ability to address users’ complex, overlapping challenges. |
| Approach: | They propose a generalizable fine-grained problem enhancement method that augments problem types, user scenarios, and profiles, enabling the construction of richer and more diverse ESC corpora. |
| Outcome: | The proposed method improves both automatic and human evaluation metrics across different models. |
ESCoT: Towards Interpretable Emotional Support Dialogue Systems (2024.acl-long)
Copied to clipboard
| Challenge: | Emotion-focused and strategy-driven chain-of-thought (ESCoT) is a new paradigm for emotional support dialogues. |
| Approach: | They propose an emotional support response generation scheme to improve interpretability . they generate a dataset and develop a model to generate dialogue responses with better interpretability. |
| Outcome: | The proposed scheme can generate dialogue responses with better interpretability. |
DecoupledESC: Enhancing Emotional Support Generation via Strategy-Response Decoupled Preference Optimization (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing ESC data entangles psychological strategies and response content, making it difficult to construct high-quality preference pairs. |
| Approach: | They propose a Decoupled ESC framework that decomposes the ESC task into two sequential subtasks: strategy planning and empathic response generation. |
| Outcome: | The proposed framework outperforms baselines, reducing preference bias and improving response quality. |
Enhancing Emotional Support Conversations: A Framework for Dynamic Knowledge Filtering and Persona Extraction (2025.coling-main)
Copied to clipboard
| Challenge: | Existing dialogue models struggle to interpret context accurately due to irrelevant or misclassified knowledge, limiting their effectiveness in real-world scenarios. |
| Approach: | They propose a framework that dynamically filters relevant commonsense knowledge and extracts personalized information to improve empathetic dialogue generation. |
| Outcome: | The proposed framework outperforms existing models in coherence, emotional understanding, and response relevance on the ESConv dataset. |
ESC-Eval: Evaluating Emotion Support Conversations in Large Language Models (2024.emnlp-main)
Copied to clipboard
Haiquan Zhao, Lingyu Li, Shisong Chen, Shuqi Kong, Jiaan Wang, Kexin Huang, Tianle Gu, Yixu Wang, Jian Wang, Liang Dandan, Zhixu Li, Yan Teng, Yanghua Xiao, Yingchun Wang
| Challenge: | Emotion Support Conversation (ESC) is a crucial application for reducing stress and providing emotional guidance. |
| Approach: | They re-organize 2,801 role-playing cards to define roles of role-players . they train a specific role- playing model called ESC-Role which behaves more like a confused person than GPT-4 . |
| Outcome: | The proposed model behaves more like a confused person than GPT-4, and the model performs better than GPLs. |