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        <title>UTD-HLTRI at SemEval 2026 Task 4: Reasoning like an Expert for Inferring Narrative Similarity</title>
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    <abstract>Narrative similarity is a challenging problem that requires reasoning over three aspects of narratives, including (1) the abstract theme; (2) the course of action and (3) the outcomes of narratives. We present UTD.HLTRISIM.NARRATIVES, our method developed for SemEval 2026 Task 4 (Narrative Story Similarity), which combines contrastive reasoning prompting with careful selection of few-shot examples to guide a Large Language Model(LLM) toward decisions of narrative comparative similarity. A curriculum learning framework orders examples of narrative triplets presented to the LLM by using a score that quantifies the impact of common narratives aspects with information discerned from several distractors of narrative similarity between pairs ofnarratives 1.</abstract>
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%0 Conference Proceedings
%T UTD-HLTRI at SemEval 2026 Task 4: Reasoning like an Expert for Inferring Narrative Similarity
%A Ailneni, Rakshitha Rao
%A Bhavsar, Maitry
%A Harabagiu, Sanda
%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 ailneni-etal-2026-utd
%X Narrative similarity is a challenging problem that requires reasoning over three aspects of narratives, including (1) the abstract theme; (2) the course of action and (3) the outcomes of narratives. We present UTD.HLTRISIM.NARRATIVES, our method developed for SemEval 2026 Task 4 (Narrative Story Similarity), which combines contrastive reasoning prompting with careful selection of few-shot examples to guide a Large Language Model(LLM) toward decisions of narrative comparative similarity. A curriculum learning framework orders examples of narrative triplets presented to the LLM by using a score that quantifies the impact of common narratives aspects with information discerned from several distractors of narrative similarity between pairs ofnarratives 1.
%U https://aclanthology.org/2026.semeval-1.433/
%P 3508-3519

Markdown (Informal)

[UTD-HLTRI at SemEval 2026 Task 4: Reasoning like an Expert for Inferring Narrative Similarity](https://aclanthology.org/2026.semeval-1.433/) (Ailneni et al., SemEval 2026)

• UTD-HLTRI at SemEval 2026 Task 4: Reasoning like an Expert for Inferring Narrative Similarity (Ailneni et al., SemEval 2026)

ACL

• Rakshitha Rao Ailneni, Maitry Bhavsar, and Sanda Harabagiu. 2026. UTD-HLTRI at SemEval 2026 Task 4: Reasoning like an Expert for Inferring Narrative Similarity. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 3508–3519, San Diego, California, USA. Association for Computational Linguistics.