When LLMs Team Up: The Emergence of Collaborative Affective Computing

• URL: http://arxiv.org/abs/2506.01698v1
• Date: Mon, 02 Jun 2025 14:00:54 GMT
• Title: When LLMs Team Up: The Emergence of Collaborative Affective Computing
• Authors: Wenna Lai, Haoran Xie, Guandong Xu, Qing Li, S. Joe Qin,
• Abstract summary: This survey aims to provide a comprehensive overview of Large Language Models (LLMs)-based collaboration systems in Affective Computing (AC)<n>LLMs offer a unified approach to affective understanding and generation tasks, enhancing the potential for dynamic, real-time interactions.<n>This work is the first to systematically explore collaborative intelligence with LLMs in AC, paving the way for more powerful applications that approach human-like social intelligence.
• Score: 17.777196145195866
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Affective Computing (AC) is essential in bridging the gap between human emotional experiences and machine understanding. Traditionally, AC tasks in natural language processing (NLP) have been approached through pipeline architectures, which often suffer from structure rigidity that leads to inefficiencies and limited adaptability. The advent of Large Language Models (LLMs) has revolutionized this field by offering a unified approach to affective understanding and generation tasks, enhancing the potential for dynamic, real-time interactions. However, LLMs face cognitive limitations in affective reasoning, such as misinterpreting cultural nuances or contextual emotions, and hallucination problems in decision-making. To address these challenges, recent research advocates for LLM-based collaboration systems that emphasize interactions among specialized models and LLMs, mimicking human-like affective intelligence through the synergy of emotional and rational thinking that aligns with Dual Process Theory in psychology. This survey aims to provide a comprehensive overview of LLM-based collaboration systems in AC, exploring from structured collaborations to autonomous collaborations. Specifically, it includes: (1) A systematic review of existing methods, focusing on collaboration strategies, mechanisms, key functions, and applications; (2) Experimental comparisons of collaboration strategies across representative tasks in affective understanding and generation; (3) An analysis highlighting the potential of these systems to enhance robustness and adaptability in complex affective reasoning; (4) A discussion of key challenges and future research directions to further advance the field. This work is the first to systematically explore collaborative intelligence with LLMs in AC, paving the way for more powerful applications that approach human-like social intelligence.

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