# An Iterative Associative Memory Model for Empathetic Response Generation

arXiv: https://arxiv.org/abs/2402.17959 · HTML: https://mlsanity.com/paper/2402.17959/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 38 references that are in the map.
- First posted on arXiv 28 Feb 2024.

## Builds on (most cited first, 21 of 21)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [Is ChatGPT a General-Purpose Natural Language Processing Task Solver?](https://mlsanity.com/paper/2302.06476/) (2023)
- [LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset](https://mlsanity.com/paper/2309.11998/) (2023)
- [Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents](https://mlsanity.com/paper/2304.09542/) (2023)
- [Towards Emotional Support Dialog Systems](https://mlsanity.com/paper/2106.01144/) (2021)
- [COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs](https://mlsanity.com/paper/2010.05953/) (2020)
- [On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective](https://mlsanity.com/paper/2302.12095/) (2023)
- [The Science of Detecting LLM-Generated Texts](https://mlsanity.com/paper/2303.07205/) (2023)
- [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)
- [Towards Persona-Based Empathetic Conversational Models](https://mlsanity.com/paper/2004.12316/) (2020)
- [Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes](https://mlsanity.com/paper/2109.08828/) (2021)
- [CoMAE: A Multi-factor Hierarchical Framework for Empathetic Response Generation](https://mlsanity.com/paper/2105.08316/) (2021)
- [Empathetic Dialogue Generation via Sensitive Emotion Recognition and Sensible Knowledge Selection](https://mlsanity.com/paper/2210.11715/) (2022)
- [CASE: Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generation](https://mlsanity.com/paper/2208.08845/) (2022)
- [Don't Lose Yourself! Empathetic Response Generation via Explicit Self-Other Awareness](https://mlsanity.com/paper/2210.03884/) (2022)
- [Improving Empathetic Dialogue Generation by Dynamically Infusing Commonsense Knowledge](https://mlsanity.com/paper/2306.04657/) (2023)
- [Exploiting Emotion-Semantic Correlations for Empathetic Response Generation](https://mlsanity.com/paper/2402.17437/) (2024)

## Built on by (most cited first, 8 of 8)

- [Synth-Empathy: Towards High-Quality Synthetic Empathy Data](https://mlsanity.com/paper/2407.21669/) (2024)
- [Dynamic Affective Memory Management for Personalized LLM Agents](https://mlsanity.com/paper/2510.27418/) (2025)
- [TOOL-ED: Enhancing Empathetic Response Generation with the Tool Calling Capability of LLM](https://mlsanity.com/paper/2412.03096/) (2024)
- [A Multi-Agent Framework with Structured Reasoning and Reflective Refinement for Multimodal Empathetic Response Generation](https://mlsanity.com/paper/2604.18988/) (2026)
- [When LLMs Team Up: The Emergence of Collaborative Affective Computing](https://mlsanity.com/paper/2506.01698/) (2025)
- [Efficient-Empathy: Towards Efficient and Effective Selection of Empathy Data](https://mlsanity.com/paper/2407.01937/) (2024)
- [PRIDE: Privileged Information-enhanced Distillation for Empathetic Dialogue Generation](https://mlsanity.com/paper/2606.23124/) (2026)
- [Dynamic Commonsense Coordination for Empathetic Response Generation](https://mlsanity.com/paper/2607.22136/) (2026)

## Abstract (arXiv)

Empathetic response generation aims to comprehend the cognitive and emotional states in dialogue utterances and generate proper responses. Psychological theories posit that comprehending emotional and cognitive states necessitates iteratively capturing and understanding associated words across dialogue utterances. However, existing approaches regard dialogue utterances as either a long sequence or independent utterances for comprehension, which are prone to overlook the associated words between them. To address this issue, we propose an Iterative Associative Memory Model (IAMM) for empathetic response generation. Specifically, we employ a novel second-order interaction attention mechanism to iteratively capture vital associated words between dialogue utterances and situations, dialogue history, and a memory module (for storing associated words), thereby accurately and nuancedly comprehending the utterances. We conduct experiments on the Empathetic-Dialogue dataset. Both automatic and human evaluations validate the efficacy of the model. Variant experiments on LLMs also demonstrate that attending to associated words improves empathetic comprehension and expression.