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        <title>schmerle at SemEval-2026 Task 4: Exploring Large Language Model Prompting Strategies for Low-Resource Narrative Similarity Detection</title>
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            <title>Proceedings of the 20th International Workshop on Semantic Evaluation (2026)</title>
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            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Kochmar</namePart>
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            <namePart type="given">Kai</namePart>
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    <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).</abstract>
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%0 Conference Proceedings
%T schmerle at SemEval-2026 Task 4: Exploring Large Language Model Prompting Strategies for Low-Resource Narrative Similarity Detection
%A Schmerle, Maximilian
%A Hellwig, Nils Constantin
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F schmerle-hellwig-2026-schmerle
%X 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).
%U https://aclanthology.org/2026.semeval-1.257/
%P 2046-2056

Markdown (Informal)

[schmerle at SemEval-2026 Task 4: Exploring Large Language Model Prompting Strategies for Low-Resource Narrative Similarity Detection](https://aclanthology.org/2026.semeval-1.257/) (Schmerle & Hellwig, SemEval 2026)

• schmerle at SemEval-2026 Task 4: Exploring Large Language Model Prompting Strategies for Low-Resource Narrative Similarity Detection (Schmerle & Hellwig, SemEval 2026)

ACL

• Maximilian Schmerle and Nils Constantin Hellwig. 2026. schmerle at SemEval-2026 Task 4: Exploring Large Language Model Prompting Strategies for Low-Resource Narrative Similarity Detection. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 2046–2056, San Diego, California, USA. Association for Computational Linguistics.