# Recent Advancement of Emotion Cognition in Large Language Models

arXiv: https://arxiv.org/abs/2409.13354 · HTML: https://mlsanity.com/paper/2409.13354/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 82 references that are in the map.
- First posted on arXiv 20 Sep 2024.

## Builds on (most cited first, 30 of 40)

- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Character-LLM: A Trainable Agent for Role-Playing](https://mlsanity.com/paper/2310.10158/) (2023)
- [Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks](https://mlsanity.com/paper/2302.08399/) (2023)
- [Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs](https://mlsanity.com/paper/2210.13312/) (2022)
- [Understanding Social Reasoning in Language Models with Language Models](https://mlsanity.com/paper/2306.15448/) (2023)
- [Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Models](https://mlsanity.com/paper/2305.14763/) (2023)
- [Large Language Models Can Learn Temporal Reasoning](https://mlsanity.com/paper/2401.06853/) (2024)
- [When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment](https://mlsanity.com/paper/2210.01478/) (2022)
- [Better to Ask in English: Cross-Lingual Evaluation of Large Language Models for Healthcare Queries](https://mlsanity.com/paper/2310.13132/) (2023)
- [AugESC: Dialogue Augmentation with Large Language Models for Emotional Support Conversation](https://mlsanity.com/paper/2202.13047/) (2022)
- [How FaR Are Large Language Models From Agents with Theory-of-Mind?](https://mlsanity.com/paper/2310.03051/) (2023)
- [Turning large language models into cognitive models](https://mlsanity.com/paper/2306.03917/) (2023)
- [Do Large Language Models know what humans know?](https://mlsanity.com/paper/2209.01515/) (2022)
- [CharacterChat: Learning towards Conversational AI with Personalized Social Support](https://mlsanity.com/paper/2308.10278/) (2023)
- [The Next Chapter: A Study of Large Language Models in Storytelling](https://mlsanity.com/paper/2301.09790/) (2023)
- [Harnessing the Power of Large Language Models for Empathetic Response Generation: Empirical Investigations and Improvements](https://mlsanity.com/paper/2310.05140/) (2023)
- [SentimentGPT: Exploiting GPT for Advanced Sentiment Analysis and its Departure from Current Machine Learning](https://mlsanity.com/paper/2307.10234/) (2023)
- [Large Language Models Produce Responses Perceived to be Empathic](https://mlsanity.com/paper/2403.18148/) (2024)
- [Time Series Modeling for Heart Rate Prediction: From ARIMA to Transformers](https://mlsanity.com/paper/2406.12199/) (2024)
- [Harnessing Earnings Reports for Stock Predictions: A QLoRA-Enhanced LLM Approach](https://mlsanity.com/paper/2408.06634/) (2024)
- [MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization](https://mlsanity.com/paper/2407.04118/) (2024)
- [Grow-and-Clip: Informative-yet-Concise Evidence Distillation for Answer Explanation](https://mlsanity.com/paper/2201.05088/) (2022)
- [Does ChatGPT have Theory of Mind?](https://mlsanity.com/paper/2305.14020/) (2023)
- [Refashioning Emotion Recognition Modelling: The Advent of Generalised Large Models](https://mlsanity.com/paper/2308.11578/) (2023)
- [Evaluating Modern Approaches in 3D Scene Reconstruction: NeRF vs Gaussian-Based Methods](https://mlsanity.com/paper/2408.04268/) (2024)
- [Predicting Information Pathways Across Online Communities](https://mlsanity.com/paper/2306.02259/) (2023)
- [NEVLP: Noise-Robust Framework for Efficient Vision-Language Pre-training](https://mlsanity.com/paper/2409.09582/) (2024)
- [PrivacyMind: Large Language Models Can Be Contextual Privacy Protection Learners](https://mlsanity.com/paper/2310.02469/) (2023)
- [XMeCap: Meme Caption Generation with Sub-Image Adaptability](https://mlsanity.com/paper/2407.17152/) (2024)
- [Can Pre-trained Language Models Understand Chinese Humor?](https://mlsanity.com/paper/2407.04105/) (2024)

## Built on by (most cited first, 9 of 9)

- [Large Language Models for Subjective Language Understanding: A Survey](https://mlsanity.com/paper/2508.07959/) (2025)
- [Domain Specific Benchmarks for Evaluating Multimodal Large Language Models](https://mlsanity.com/paper/2506.12958/) (2025)
- [Enhancing Exchange Rate Forecasting with Explainable Deep Learning Models](https://mlsanity.com/paper/2410.19241/) (2024)
- [From Joy to Fear: A Benchmark of Emotion Estimation in Pop Song Lyrics](https://mlsanity.com/paper/2509.05617/) (2025)
- [Fuzzy Reasoning Chain (FRC): An Innovative Reasoning Framework from Fuzziness to Clarity](https://mlsanity.com/paper/2509.22054/) (2025)
- [Pre-trained Graphformer-based Ranking at Web-scale Search (Extended Abstract)](https://mlsanity.com/paper/2409.16590/) (2024)
- [Generative Pre-trained Ranking Model with Over-parameterization at Web-Scale (Extended Abstract)](https://mlsanity.com/paper/2409.16594/) (2024)
- [Enhancing Collective Intelligence in Large Language Models Through Emotional Integration](https://mlsanity.com/paper/2503.04849/) (2025)
- [ELSA: A Style Aligned Dataset for Emotionally Intelligent Language Generation](https://mlsanity.com/paper/2504.08281/) (2025)

## Abstract (arXiv)

Emotion cognition in large language models (LLMs) is crucial for enhancing performance across various applications, such as social media, human-computer interaction, and mental health assessment. We explore the current landscape of research, which primarily revolves around emotion classification, emotionally rich response generation, and Theory of Mind assessments, while acknowledge the challenges like dependency on annotated data and complexity in emotion processing. In this paper, we present a detailed survey of recent progress in LLMs for emotion cognition. We explore key research studies, methodologies, outcomes, and resources, aligning them with Ulric Neisser's cognitive stages. Additionally, we outline potential future directions for research in this evolving field, including unsupervised learning approaches and the development of more complex and interpretable emotion cognition LLMs. We also discuss advanced methods such as contrastive learning used to improve LLMs' emotion cognition capabilities.