4 papers · 1 filter
RAM-SD: Retrieval-Augmented Multi-agent framework for Sarcasm Detection
Ziyang Zhou, Ziqi Liu, Yan Wang +2
Sarcasm detection remains a significant challenge due to its reliance on nuanced contextual understanding, world knowledge, and multi-faceted linguistic cues that vary substantiall…
Following the TRACE: A Structured Path to Empathetic Response Generation with Multi-Agent Models
Ziqi Liu, Ziyang Zhou, Yilin Li +2
Empathetic response generation is a crucial task for creating more human-like and supportive conversational agents. However, existing methods face a core trade-off between the anal…
SEVADE: Self-Evolving Multi-Agent Analysis with Decoupled Evaluation for Hallucination-Resistant Irony Detection
Ziqi Liu, Ziyang Zhou, Yilin Li +4
Sarcasm detection is a crucial yet challenging Natural Language Processing task. Existing Large Language Model methods are often limited by single-perspective analysis, static reas…
CAF-I: A Collaborative Multi-Agent Framework for Enhanced Irony Detection with Large Language Models
Ziqi. Liu, Ziyang. Zhou, Mingxuan. Hu
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-…