MoPrO-SD: Modular Prompt Optimization for Stance Detection
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 p...
| Authors | Meysam Sabbaghan, Nikesh Gyawali, Arman Zareian Jahromi et al. |
|---|---|
| Primary category | cs.CL |
| Published | 9 October 2026 |
| Last updated at source | 9 October 2026 |
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