# Emotional Support with LLM-based Empathetic Dialogue Generation

arXiv: https://arxiv.org/abs/2507.12820 · HTML: https://mlsanity.com/paper/2507.12820/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 47 references that are in the map.
- First posted on arXiv 17 Jul 2025.

## Builds on (most cited first, 24 of 24)

- [Towards Emotional Support Dialog Systems](https://mlsanity.com/paper/2106.01144/) (2021)
- [Can Large Language Models be Good Emotional Supporter? Mitigating Preference Bias on Emotional Support Conversation](https://mlsanity.com/paper/2402.13211/) (2024)
- [Muddit: Liberating Generation Beyond Text-to-Image with a Unified Discrete Diffusion Model](https://mlsanity.com/paper/2505.23606/) (2025)
- [AI Flow at the Network Edge](https://mlsanity.com/paper/2411.12469/) (2024)
- [PAL: Persona-Augmented Emotional Support Conversation Generation](https://mlsanity.com/paper/2212.09235/) (2022)
- [Table-R1: Region-based Reinforcement Learning for Table Understanding](https://mlsanity.com/paper/2505.12415/) (2025)
- [TransESC: Smoothing Emotional Support Conversation via Turn-Level State Transition](https://mlsanity.com/paper/2305.03296/) (2023)
- [Towards Multimodal Emotional Support Conversation Systems](https://mlsanity.com/paper/2408.03650/) (2024)
- [Lemur: Log Parsing with Entropy Sampling and Chain-of-Thought Merging](https://mlsanity.com/paper/2402.18205/) (2024)
- [Facilitating Multi-turn Emotional Support Conversation with Positive Emotion Elicitation: A Reinforcement Learning Approach](https://mlsanity.com/paper/2307.07994/) (2023)
- [TeleChat Technical Report](https://mlsanity.com/paper/2401.03804/) (2024)
- [Tele-FLM Technical Report](https://mlsanity.com/paper/2404.16645/) (2024)
- [Knowledge-enhanced Memory Model for Emotional Support Conversation](https://mlsanity.com/paper/2310.07700/) (2023)
- [MLR: A Two-stage Conversational Query Rewriting Model with Multi-task Learning](https://mlsanity.com/paper/2004.05812/) (2020)
- [An Empathetic AI Coach for Self-Attachment Therapy](https://mlsanity.com/paper/2209.08316/) (2022)
- [Dynamic Demonstration Retrieval and Cognitive Understanding for Emotional Support Conversation](https://mlsanity.com/paper/2404.02505/) (2024)
- [Detecting Concept-level Emotion Cause in Microblogging](https://mlsanity.com/paper/1504.08050/) (2015)
- [CauESC: A Causal Aware Model for Emotional Support Conversation](https://mlsanity.com/paper/2401.17755/) (2024)
- [Classifying and Ranking Microblogging Hashtags with News Categories](https://mlsanity.com/paper/1505.00862/) (2015)
- [TCNN: Triple Convolutional Neural Network Models for Retrieval-based Question Answering System in E-commerce](https://mlsanity.com/paper/2004.10919/) (2020)
- [Towards Robustness and Diversity: Continual Learning in Dialog Generation with Text-Mixup and Batch Nuclear-Norm Maximization](https://mlsanity.com/paper/2403.10894/) (2024)
- [EmoFSM: A Finite State Machine for Emotional Support Conversation](https://mlsanity.com/paper/2504.11837/) (2025)
- [An Emotion-controlled Dialog Response Generation Model with Dynamic Vocabulary](https://mlsanity.com/paper/2103.02878/) (2021)
- [Mining Interest Trends and Adaptively Assigning SampleWeight for Session-based Recommendation](https://mlsanity.com/paper/2306.11610/) (2023)

## Built on by (most cited first, 4 of 4)

- [PsychEthicsBench: Evaluating Large Language Models Against Australian Mental Health Ethics](https://mlsanity.com/paper/2601.03578/) (2026)
- [First, Do No Harm: AI Supervisor Scaffolds Novice Growth in Counselor Education](https://mlsanity.com/paper/2508.09042/) (2025)
- [Multi-Intent Spoken Language Understanding: Methods, Trends, and Challenges](https://mlsanity.com/paper/2512.11258/) (2025)
- [From Pattern Recognizers to Personalized Companions: A Survey of Large Language Models in Mental Health](https://mlsanity.com/paper/2609.25186/) (2026)

## Abstract (arXiv)

Emotional Support Conversation (ESC) aims to provide empathetic and effective emotional assistance through dialogue, addressing the growing demand for mental health support. This paper presents our solution for the NLPCC 2025 Task 8 ESC evaluation, where we leverage large-scale language models enhanced by prompt engineering and finetuning techniques. We explore both parameter-efficient Low-Rank Adaptation and full-parameter fine-tuning strategies to improve the model's ability to generate supportive and contextually appropriate responses. Our best model ranked second in the competition, highlighting the potential of combining LLMs with effective adaptation methods for ESC tasks. Future work will focus on further enhancing emotional understanding and response personalization to build more practical and reliable emotional support systems.