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.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2604.26319 [cs.CL] |
| (or arXiv:2604.26319v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2604.26319 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Bowen Zhang [view email]
[v1]
Wed, 29 Apr 2026 06:02:32 UTC (54 KB)