# Dynamic Demonstration Retrieval and Cognitive Understanding for Emotional Support Conversation

arXiv: https://arxiv.org/abs/2404.02505 · HTML: https://mlsanity.com/paper/2404.02505/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 48 references that are in the map.
- First posted on arXiv 3 Apr 2024.

## Builds on (most cited first, 26 of 26)

- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://mlsanity.com/paper/2202.12837/) (2022)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Retrieval Augmentation Reduces Hallucination in Conversation](https://mlsanity.com/paper/2104.07567/) (2021)
- [Recipes for building an open-domain chatbot](https://mlsanity.com/paper/2004.13637/) (2020)
- [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://mlsanity.com/paper/1906.05317/) (2019)
- [Towards Emotional Support Dialog Systems](https://mlsanity.com/paper/2106.01144/) (2021)
- [Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory](https://mlsanity.com/paper/1704.01074/) (2017)
- [MIME: MIMicking Emotions for Empathetic Response Generation](https://mlsanity.com/paper/2010.01454/) (2020)
- [CEM: Commonsense-aware Empathetic Response Generation](https://mlsanity.com/paper/2109.05739/) (2021)
- [Generating Sentences by Editing Prototypes](https://mlsanity.com/paper/1709.08878/) (2017)
- [Retrieve and Refine: Improved Sequence Generation Models For Dialogue](https://mlsanity.com/paper/1808.04776/) (2018)
- [Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach](https://mlsanity.com/paper/2101.07714/) (2021)
- [FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation](https://mlsanity.com/paper/2209.14290/) (2022)
- [Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning](https://mlsanity.com/paper/2210.04242/) (2022)
- [Control Globally, Understand Locally: A Global-to-Local Hierarchical Graph Network for Emotional Support Conversation](https://mlsanity.com/paper/2204.12749/) (2022)
- [Knowledge Bridging for Empathetic Dialogue Generation](https://mlsanity.com/paper/2009.09708/) (2020)
- [Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory](https://mlsanity.com/paper/1809.05296/) (2018)
- [Response Generation by Context-aware Prototype Editing](https://mlsanity.com/paper/1806.07042/) (2018)
- [PAL: Persona-Augmented Emotional Support Conversation Generation](https://mlsanity.com/paper/2212.09235/) (2022)
- [DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation](https://mlsanity.com/paper/2204.13031/) (2022)
- [TransESC: Smoothing Emotional Support Conversation via Turn-Level State Transition](https://mlsanity.com/paper/2305.03296/) (2023)
- [Controlling Dialogue Generation with Semantic Exemplars](https://mlsanity.com/paper/2008.09075/) (2020)
- [FADO: Feedback-Aware Double COntrolling Network for Emotional Support Conversation](https://mlsanity.com/paper/2211.00250/) (2022)
- [A Sequential Matching Framework for Multi-turn Response Selection in Retrieval-based Chatbots](https://mlsanity.com/paper/1710.11344/) (2017)
- [Exemplars-guided Empathetic Response Generation Controlled by the Elements of Human Communication](https://mlsanity.com/paper/2106.11791/) (2021)
- [A Personalized Dense Retrieval Framework for Unified Information Access](https://mlsanity.com/paper/2304.13654/) (2023)

## Built on by (most cited first, 5 of 5)

- [DemoRank: Selecting Effective Demonstrations for Large Language Models in Ranking Task](https://mlsanity.com/paper/2406.16332/) (2024)
- [Emotional Support with LLM-based Empathetic Dialogue Generation](https://mlsanity.com/paper/2507.12820/) (2025)
- [Mind2: Mind-to-Mind Emotional Support System with Bidirectional Cognitive Discourse Analysis](https://mlsanity.com/paper/2503.16523/) (2025)
- [PRCCF: A Persona-guided Retrieval and Causal-aware Cognitive Filtering Framework for Emotional Support Conversation](https://mlsanity.com/paper/2604.01671/) (2026)
- [MindTailor: Personalized Emotional Support via Post History-Grounded Case Formulation and Collaborative Refinement](https://mlsanity.com/paper/2606.21930/) (2026)

## Abstract (arXiv)

Emotional Support Conversation (ESC) systems are pivotal in providing empathetic interactions, aiding users through negative emotional states by understanding and addressing their unique experiences. In this paper, we tackle two key challenges in ESC: enhancing contextually relevant and empathetic response generation through dynamic demonstration retrieval, and advancing cognitive understanding to grasp implicit mental states comprehensively. We introduce Dynamic Demonstration Retrieval and Cognitive-Aspect Situation Understanding (ourwork), a novel approach that synergizes these elements to improve the quality of support provided in ESCs. By leveraging in-context learning and persona information, we introduce an innovative retrieval mechanism that selects informative and personalized demonstration pairs. We also propose a cognitive understanding module that utilizes four cognitive relationships from the ATOMIC knowledge source to deepen situational awareness of help-seekers' mental states. Our supportive decoder integrates information from diverse knowledge sources, underpinning response generation that is both empathetic and cognitively aware. The effectiveness of ourwork is demonstrated through extensive automatic and human evaluations, revealing substantial improvements over numerous state-of-the-art models, with up to 13.79% enhancement in overall performance of ten metrics. Our codes are available for public access to facilitate further research and development.