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        <title>Few-Shot Coreference Resolution with Semantic Difficulty Metrics and In-Context Learning</title>
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    <name type="personal">
        <namePart type="given">Nguyen</namePart>
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            <title>Proceedings of the Eighth Workshop on Computational Models of Reference, Anaphora and Coreference</title>
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            <namePart type="given">Maciej</namePart>
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            <namePart type="given">Michal</namePart>
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    <abstract>This paper presents our submission to the CRAC 2025 Shared Task on Multilingual Coreference Resolution in the LLM track. We propose a prompt-based few-shot coreference resolution system where the final inference is performed by Grok-3 using in-context learning. The core of our methodology is a difficulty- aware sample selection pipeline that leverages Gemini Flash 2.0 to compute semantic diffi- culty metrics, including mention dissimilarity and pronoun ambiguity. By identifying and selecting the most challenging training sam- ples for each language, we construct highly informative prompts to guide Grok-3 in predict- ing coreference chains and reconstructing zero anaphora. Our approach secured 3rd place in the CRAC 2025 shared task.</abstract>
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%0 Conference Proceedings
%T Few-Shot Coreference Resolution with Semantic Difficulty Metrics and In-Context Learning
%A Phuc, Nguyen Xuan
%A Thin, Dang Van
%Y Ogrodniczuk, Maciej
%Y Novak, Michal
%Y Poesio, Massimo
%Y Pradhan, Sameer
%Y Ng, Vincent
%S Proceedings of the Eighth Workshop on Computational Models of Reference, Anaphora and Coreference
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%F phuc-thin-2025-shot
%X This paper presents our submission to the CRAC 2025 Shared Task on Multilingual Coreference Resolution in the LLM track. We propose a prompt-based few-shot coreference resolution system where the final inference is performed by Grok-3 using in-context learning. The core of our methodology is a difficulty- aware sample selection pipeline that leverages Gemini Flash 2.0 to compute semantic diffi- culty metrics, including mention dissimilarity and pronoun ambiguity. By identifying and selecting the most challenging training sam- ples for each language, we construct highly informative prompts to guide Grok-3 in predict- ing coreference chains and reconstructing zero anaphora. Our approach secured 3rd place in the CRAC 2025 shared task.
%U https://aclanthology.org/2025.crac-1.13/
%P 149-153

Markdown (Informal)

[Few-Shot Coreference Resolution with Semantic Difficulty Metrics and In-Context Learning](https://aclanthology.org/2025.crac-1.13/) (Phuc & Thin, CRAC 2025)

• Few-Shot Coreference Resolution with Semantic Difficulty Metrics and In-Context Learning (Phuc & Thin, CRAC 2025)

ACL

• Nguyen Xuan Phuc and Dang Van Thin. 2025. Few-Shot Coreference Resolution with Semantic Difficulty Metrics and In-Context Learning. In Proceedings of the Eighth Workshop on Computational Models of Reference, Anaphora and Coreference, pages 149–153, Suzhou, China. Association for Computational Linguistics.