MoPrO-SD：面向立场检测的模块化提示词优化框架

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Abstract:Stance detection remains challenging for large language models (LLMs) due to implicit opinions, sarcasm, and context-dependent expressions of stance. Although automatic prompt optimization offers an alternative to fine-tuning without updating model parameters, existing structured approaches are not specifically designed around stance-label semantics and class-specific ambiguities. We introduce MoPrO-SD, a stance-specific prompt optimization framework that decomposes prompts into functional semantic modules and refines them sequentially using LLM-based feedback. We study three refinement strategies: instruction-only optimization (I), instruction with reasoning (I+R), and instruction with per-example reasoning (I+R+E), using class-conditioned errors for stance-label refinement. We also introduce VaccineStance, a new human-annotated benchmark for vaccine stance detection. Experiments across four datasets and five open-weight LLMs show that MoPrO-SD improves upon the human-written Base prompt in many settings. On SemEval-2016, class-conditioned feedback achieves higher mean Macro-F1 than pooled feedback for four of five backbones. Both reasoning-based strategies, I+R and I+R+E, achieve strong performance, with their relative effectiveness varying across models and datasets, while remaining competitive with recent prompt optimization methods. The MoPrO-SD code and VaccineStance dataset are available on GitHub.

Subjects:	Computation and Language (cs.CL)
Cite as:	arXiv:2610.13386 [cs.CL]
	(or arXiv:2610.13386v1 [cs.CL] for this version)
	https://doi.org/10.48550/arXiv.2610.13386 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Meysam Sabbaghan [view email]
[v1] Fri, 9 Oct 2026 11:00:33 UTC (6,615 KB)