Exploring Multi-Agent Debate for Zero-Shot Stance Detection: A Novel Approach

Applied Sciences, Год журнала: 2025, Номер 15(9), С. 4612 - 4612

Опубликована: Апрель 22, 2025

Zero-shot stance detection aims to identify the expressed in social media text aimed at specific targets without relying on annotated data. However, due insufficient contextual information and inherent ambiguity of language, this task faces numerous challenges low-resource scenarios. This work proposes a novel zero-shot method based multi-agent debate (ZSMD) address aforementioned challenges. Specifically, we construct two debater agents representing supporting opposing stances. A knowledge enhancement module supplements original tweet target with relevant background knowledge, providing richer support for argument generation. Subsequently, engage over predetermined number rounds, employing rebuttal strategies such as factual verification, logical analysis, sentiment analysis. If no consensus is reached within specified referee agent synthesizes process input deliver final determination. We evaluate ZSMD benchmark datasets, SemEval-2016 Task 6 P-Stance, compare it against strong baselines MB-Cal COLA. The experimental results show that not only achieves higher accuracy than these baselines, but also provides deeper insights into subtle differences opinion expression, highlighting potential structured argumentation settings.

Язык: Английский

MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark

Neurocomputing, Год журнала: 2025, Номер unknown, С. 130490 - 130490

Опубликована: Май 1, 2025

Язык: Английский

LLM Chain Ensembles for Scalable and Accurate Data Annotation

2021 IEEE International Conference on Big Data (Big Data), Год журнала: 2024, Номер unknown, С. 2110 - 2118

Опубликована: Дек. 15, 2024

Язык: Английский