# APTNESS: Incorporating Appraisal Theory and Emotion Support Strategies for Empathetic Response Generation

arXiv: https://arxiv.org/abs/2407.21048 · HTML: https://mlsanity.com/paper/2407.21048/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 37 references that are in the map.
- First posted on arXiv 23 Jul 2024.

## Builds on (most cited first, 16 of 16)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Unsupervised Dense Information Retrieval with Contrastive Learning](https://mlsanity.com/paper/2112.09118/) (2021)
- [Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering](https://mlsanity.com/paper/2007.01282/) (2020)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Towards Emotional Support Dialog Systems](https://mlsanity.com/paper/2106.01144/) (2021)
- [Nomic Embed: Training a Reproducible Long Context Text Embedder](https://mlsanity.com/paper/2402.01613/) (2024)
- [COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs](https://mlsanity.com/paper/2010.05953/) (2020)
- [CEM: Commonsense-aware Empathetic Response Generation](https://mlsanity.com/paper/2109.05739/) (2021)
- [Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2310.14696/) (2023)
- [Harnessing the Power of Large Language Models for Empathetic Response Generation: Empirical Investigations and Improvements](https://mlsanity.com/paper/2310.05140/) (2023)
- [Merging Generated and Retrieved Knowledge for Open-Domain QA](https://mlsanity.com/paper/2310.14393/) (2023)
- [Generative Relevance Feedback with Large Language Models](https://mlsanity.com/paper/2304.13157/) (2023)
- [Rational Sensibility: LLM Enhanced Empathetic Response Generation Guided by Self-presentation Theory](https://mlsanity.com/paper/2312.08702/) (2023)

## Built on by (most cited first, 12 of 12)

- [Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspective](https://mlsanity.com/paper/2408.04638/) (2024)
- [Are LLMs Empathetic to All? Investigating the Influence of Multi-Demographic Personas on a Model's Empathy](https://mlsanity.com/paper/2510.10328/) (2025)
- [ReflectDiffu:Reflect between Emotion-intent Contagion and Mimicry for Empathetic Response Generation via a RL-Diffusion Framework](https://mlsanity.com/paper/2409.10289/) (2024)
- [MGHFT: Multi-Granularity Hierarchical Fusion Transformer for Cross-Modal Sticker Emotion Recognition](https://mlsanity.com/paper/2507.18929/) (2025)
- [CTR-Sink: Attention Sink for Language Models in Click-Through Rate Prediction](https://mlsanity.com/paper/2508.03668/) (2025)
- [AI generates well-liked but templatic empathic responses](https://mlsanity.com/paper/2604.08479/) (2026)
- [Emotion and Intention Guided Multi-Modal Learning for Sticker Response Selection](https://mlsanity.com/paper/2511.17587/) (2025)
- [Dual Knowledge-Enhanced Two-Stage Reasoner for Multimodal Dialog Systems](https://mlsanity.com/paper/2509.07817/) (2025)
- [Towards High-Level Semantic Intelligence](https://mlsanity.com/paper/2607.24082/) (2026)
- [Empowering Cross-Domain Sequential Recommendation with Hybrid Tokenization and Serial-Parallel Decoding](https://mlsanity.com/paper/2607.28659/) (2026)
- [ESCRAG-R1: Retrieval-Augmented Reinforcement Learning for Emotional Support Conversation](https://mlsanity.com/paper/2608.21925/) (2026)
- [From Momentary Emotion Inference to Sustained Emotion Support: Evaluating a Companion Agent in a Longitudinal Study](https://mlsanity.com/paper/2609.16344/) (2026)

## Abstract (arXiv)

Empathetic response generation is designed to comprehend the emotions of others and select the most appropriate strategies to assist them in resolving emotional challenges. Empathy can be categorized into cognitive empathy and affective empathy. The former pertains to the ability to understand and discern the emotional issues and situations of others, while the latter involves the capacity to provide comfort. To enhance one's empathetic abilities, it is essential to develop both these aspects. Therefore, we develop an innovative framework that combines retrieval augmentation and emotional support strategy integration. Our framework starts with the introduction of a comprehensive emotional palette for empathy. We then apply appraisal theory to decompose this palette and create a database of empathetic responses. This database serves as an external resource and enhances the LLM's empathy by integrating semantic retrieval mechanisms. Moreover, our framework places a strong emphasis on the proper articulation of response strategies. By incorporating emotional support strategies, we aim to enrich the model's capabilities in both cognitive and affective empathy, leading to a more nuanced and comprehensive empathetic response. Finally, we extract datasets ED and ET from the empathetic dialogue dataset textscEmpatheticDialogues and ExTES based on dialogue length. Experiments demonstrate that our framework can enhance the empathy ability of LLMs from both cognitive and affective empathy perspectives. Our code is released at https://github.com/CAS-SIAT-XinHai/APTNESS.