CuriosAI at SemEval-2026 Task 4: A Comprehensive Study of Zero-Shot versus Fine-Tuned Approaches for Narrative Similarity

AbstractThis paper presents our system for SemEval-2026 Task 4 on narrative similarity assessment.Through comprehensive experimentation, we evaluated various approaches including zero-shot pre-trained models, prompt engineering with large language models, and multiple fine-tuning strategies using synthetic data. Our experiments revealed a surprising finding: pre-trained sentence transformers in a zero-shot setting consistently outperformed all fine-tuning attempts. Specifically, our best system using sentence-transformers/sentence-t5-xl achieved 67.5% accuracy on the development set (95% CI: [61.0%, 74.0%]), while all fine-tuning approaches resulted in performance degradation of 2-18 percentage points. We provide a detailed analysis of why fine-tuning failed and discuss the implications for narrative similarity tasks.