CAF-I: A Collaborative Multi-Agent Framework for Enhanced Irony Detection with Large Language Models

• URL: http://arxiv.org/abs/2506.08430v2
• Date: Thu, 12 Jun 2025 05:41:40 GMT
• Title: CAF-I: A Collaborative Multi-Agent Framework for Enhanced Irony Detection with Large Language Models
• Authors: Ziqi. Liu, Ziyang. Zhou, Mingxuan. Hu,
• Abstract summary: This paper introduces the Collaborative Agent Framework for Irony (CAF-I)<n>CAF-I employs specialized agents for Context, Semantics, and Rhetoric, which perform multidimensional analysis.<n> Experiments on benchmark datasets establish CAF-I's state-of-the-art zero-shot performance.
• Score: 10.551915512812107
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language model (LLM) have become mainstream methods in the field of sarcasm detection. However, existing LLM methods face challenges in irony detection, including: 1. single-perspective limitations, 2. insufficient comprehensive understanding, and 3. lack of interpretability. This paper introduces the Collaborative Agent Framework for Irony (CAF-I), an LLM-driven multi-agent system designed to overcome these issues. CAF-I employs specialized agents for Context, Semantics, and Rhetoric, which perform multidimensional analysis and engage in interactive collaborative optimization. A Decision Agent then consolidates these perspectives, with a Refinement Evaluator Agent providing conditional feedback for optimization. Experiments on benchmark datasets establish CAF-I's state-of-the-art zero-shot performance. Achieving SOTA on the vast majority of metrics, CAF-I reaches an average Macro-F1 of 76.31, a 4.98 absolute improvement over the strongest prior baseline. This success is attained by its effective simulation of human-like multi-perspective analysis, enhancing detection accuracy and interpretability.

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