Challenge: Emstremo aims to achieve strategic control of emotional alignment by perceiving and responding to the user’s emotions.
Approach: They propose to integrate strategies and emotions into a conversational emotional support agent called Emstremo that aims to achieve strategic control of emotional alignment by perceiving and responding to the user’s emotions.
Outcome: Emstremo achieves strategic control of emotional alignment by perceiving and responding to the user’s emotions.

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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.
Outcome: The proposed framework outperforms existing models in coherence, emotional understanding, and response relevance on the ESConv dataset.
Facilitating Multi-turn Emotional Support Conversation with Positive Emotion Elicitation: A Reinforcement Learning Approach (2023.acl-long)

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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.
DecoupledESC: Enhancing Emotional Support Generation via Strategy-Response Decoupled Preference Optimization (2025.findings-emnlp)

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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.
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.
Outcome: The proposed framework outperforms state-of-the-art methods on two ESC datasets with a significant margin (better proficiency and lower preference bias).
SweetieChat: A Strategy-Enhanced Role-playing Framework for Diverse Scenarios Handling Emotional Support Agent (2025.coling-main)

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Challenge: Large Language Models (LLMs) have demonstrated promising potential in providing empathetic support during interactions, but their responses are often verbose or overly formulaic, failing to adequately address the diverse emotional support needs of real-world scenarios.
Approach: They propose a strategy-enhanced role-playing framework that emulates real-world interactions and a dataset that is used to develop an emotional support agent.
Outcome: The proposed framework emulates real-world interactions and promotes a broader range of dialogues and Emotional Support Agent training.
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.
I Don’t Need Solution. I Need Emotional Support : Empathetic LLMs based on Emotional Validation (2026.findings-acl)

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Challenge: Existing large language models (LLMs) struggle to generate emotional support response, despite observing and reflecting on the help-seeker’s situation . Empathy drives the formation of constructive interpersonal and supportive relationships, including counseling for mental health care .
Approach: They propose to use a two-stage training process to enhance empathetic response generation through empathy acquisition and emotional validation alignment.
Outcome: The proposed method significantly improves empathetic response generation, achieving superior performance in both automatic and human evaluations.
PAL to Lend a Helping Hand: Towards Building an Emotion Adaptive Polite and Empathetic Counseling Conversational Agent (2023.acl-long)

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Challenge: The social stigma associated with mental illness prevents individuals from addressing their issues and getting assistance.
Approach: They propose to build a Polite and empAthetic conversational agent PAL to lay down the counseling support to substance addicts and crime victims.
Outcome: The proposed agent is scalable and can be easily modified with different modules of preference models as per need.
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.
Outcome: The proposed framework improves strategy selection preferences at each dialogue turn.
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.
Outcome: The proposed model infers the user’s fine-grained emotional status and responds skillfully using mixed-up strategy modeling.

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