🤖 AI Summary
Zero-shot stance detection faces three key challenges: (1) highly dynamic background knowledge, (2) targets often being composite entities or events requiring explicit semantic modeling of their relationships with stance labels, and (3) rhetorical devices (e.g., irony) obscuring the speaker’s true intent. To address these, we propose MSME—a Multi-Stage Multi-Expert framework comprising three phases: knowledge preparation, expert reasoning, and decision aggregation. MSME introduces three specialized experts: a *knowledge expert* integrating large language models with retrieval-augmented generation; a *label expert* performing fine-grained semantic modeling of stance labels; and a *pragmatic expert* equipped with irony-aware reasoning. A meta-arbitrator fuses their outputs. Evaluated on three public benchmarks, MSME significantly outperforms existing zero-shot methods, achieving new state-of-the-art performance. Results demonstrate that multi-source, collaborative reasoning substantially enhances stance identification in linguistically and semantically complex scenarios.
📝 Abstract
LLM-based approaches have recently achieved impressive results in zero-shot stance detection. However, they still struggle in complex real-world scenarios, where stance understanding requires dynamic background knowledge, target definitions involve compound entities or events that must be explicitly linked to stance labels, and rhetorical devices such as irony often obscure the author's actual intent. To address these challenges, we propose MSME, a Multi-Stage, Multi-Expert framework for zero-shot stance detection. MSME consists of three stages: (1) Knowledge Preparation, where relevant background knowledge is retrieved and stance labels are clarified; (2) Expert Reasoning, involving three specialized modules-Knowledge Expert distills salient facts and reasons from a knowledge perspective, Label Expert refines stance labels and reasons accordingly, and Pragmatic Expert detects rhetorical cues such as irony to infer intent from a pragmatic angle; (3) Decision Aggregation, where a Meta-Judge integrates all expert analyses to produce the final stance prediction. Experiments on three public datasets show that MSME achieves state-of-the-art performance across the board.