MoPrO-SD: Modular Prompt Optimization for Stance Detection
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Computer Science > Computation and Language
arXiv:2610.13386 (cs)
[Submitted on 9 Oct 2026]
Title:MoPrO-SD: Modular Prompt Optimization for Stance Detection
Authors:Meysam Sabbaghan, Nikesh Gyawali, Arman Zareian Jahromi, Sebastian Loera-Lozano, Doina Caragea, Cornelia Caragea
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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)
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View a PDF of the paper titled MoPrO-SD: Modular Prompt Optimization for Stance Detection, by Meysam Sabbaghan and 5 other authors
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