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.

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Chain of Strategy Optimization Makes Large Language Models Better Emotional Supporter (2025.findings-emnlp)

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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.
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Causal-ESC: Reliable Policy Learning for Emotional Support Conversation via Causal Inference (2026.acl-long)

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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.
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From Generic Empathy to Personalized Emotional Support: A Self-Evolution Framework for User Preference Alignment (2025.findings-emnlp)

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Challenge: Large Language Models (LLMs) show great potential for expressing empathy, but often deliver generic responses that fail to address users’ specific needs.
Approach: They propose a self-evolution framework to help LLMs improve their responses to better align with users’ implicit preferences concerning personality, emotional state, and specific context.
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Enhancing Emotional Support Conversations: A Framework for Dynamic Knowledge Filtering and Persona Extraction (2025.coling-main)

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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.
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Convert Language Model into a Value-based Strategic Planner (2025.acl-industry)

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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.
TransESC: Smoothing Emotional Support Conversation via Turn-Level State Transition (2023.findings-acl)

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Challenge: Emotion Support Conversation (ESC) is a goaldirected task with the goal of reducing the emotional distress of people.
Approach: They propose to take turn-level state Transitions of ESC from three perspectives to generate smooth transitions between utterances.
Outcome: The proposed method generates smoother and more effective responses on automatic and human evaluations.
EmoDynamiX: Emotional Support Dialogue Strategy Prediction by Modelling MiXed Emotions and Discourse Dynamics (2025.naacl-long)

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Challenge: Recent studies show that implicit strategy planning lacks transparency and that LLMs’ inherent preference bias towards certain socio-emotional strategies hinders the delivery of high-quality emotional support.
Approach: They propose to decouple strategy prediction from language generation and introduce a new dialogue strategy prediction framework, EmoDynamiX, which models the discourse dynamics between user fine-grained emotions and system strategies using a heterogeneous graph for better performance and transparency.
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MISC: A Mixed Strategy-Aware Model integrating COMET for Emotional Support Conversation (2022.acl-long)

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Challenge: Existing methods for emotional support conversation are too coarse-grained to capture user’s instant mental state and focus on expressing empathy in the response rather than gradually reducing user’ s distress.
Approach: They propose a model which firstly infers the user’s fine-grained emotional status and then responds skillfully using a mixture of strategy.
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ESC-Eval: Evaluating Emotion Support Conversations in Large Language Models (2024.emnlp-main)

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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 .
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From Personas to Talks: Revisiting the Impact of Personas on LLM-Synthesized Emotional Support Conversations (2025.emnlp-main)

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Challenge: Experimental results show that LLMs can infer persona traits and subtle shifts in emotionality and extraversion occur . scalable solutions with reduced costs and enhanced data privacy are needed .
Approach: They explore the role of personas in the creation of emotional support conversations by LLMs.
Outcome: The proposed model can infer persona traits and maintain key persona characteristics while revealing shifts in emotionality and extraversion.

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