schmerle at SemEval-2026 Task 4: Exploring Large Language Model Prompting Strategies for Low-Resource Narrative Similarity Detection
Year: 2026
Type: conference-paper
Abstract: Narrative similarity detection has broad applications in plagiarism detection, content recommendation, and comparative narrative analysis.We present a training-free, prompting-only framework for SemEval-2026 Task 4 (Track A), which requires identifying which of two candidate stories is narratively more similar to a given anchor story.Without any fine-tuning or additional annotations, we systematically evaluate three prompt templates across five structural prompting strategies, including zero-shot and few-shot inference, narrative summarization, keyword extraction, aspect splitting, and pairwise comparison.Structured prompt templates and decomposed pairwise comparisons consistently outperform baseline configurations, achieving a peak accuracy of 72.50% on the test set and 67.75% on the final leaderboard (23th out of 44 teams).
Institution: University of Regensburg
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166798801