A Systematic Comparison of Prompting and Multi-Agent Methods for LLM-based Stance Detection

Genan Dai; Zini Chen; Yi Yang; Bowen Zhang

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arXiv · 2604.26319

A Systematic Comparison of Prompting and Multi-Agent Methods for LLM-based Stance Detection

Genan Dai·Zini Chen·Yi Yang·Bowen Zhang

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Abstract

Stance detection identifies the attitude of a text author toward a given target. Recent studies have explored various LLM-based strategies for this task, from zero-shot prompting to multi-agent debate. However, existing works differ in data splits, base models, and evaluation protocols, making fair comparison difficult. We conduct a systematic comparison that evaluates five methods across two categories -- prompt-based inference (Direct Prompting, Auto-CoT, StSQA) and agent-based debate (COLA, MPRF) -- on four datasets with 14 subtasks, using 15 LLMs from six model families with parameter sizes from 7B to 72B+. Our experiments yield several findings. First, on all models with complete results, the best prompt-based method outperforms the best agent-based method, while agent methods require 7 to 12 times more API calls per sample. Second, model scale has a larger impact on performance than method choice, with gains plateauing around 32B. Third, reasoning-enhanced models (DeepSeek-R1) do not consistently outperform general models of the same size on this task.

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Genan Dai, Zini Chen, Yi Yang, Bowen Zhang. 2026-04-29. A Systematic Comparison of Prompting and Multi-Agent Methods for LLM-based Stance Detection. https://arxiv.org/abs/2604.26319

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