{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T12:07:07Z","timestamp":1764850027589,"version":"3.46.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686387","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T00:00:00Z","timestamp":1764633600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,12,2]]},"abstract":"<jats:p>Two major challenges for legal argument mining are limited annotated data and the need to model context-dependent reasoning. This paper presents a framework that enhances few-shot in-context learning (ICL) for legal argument mining by combining context-augmented similarity with hard label diversity filtering. Our method selects example sentences not only based on their semantic closeness to the target but also by incorporating their surrounding context and ensuring diverse label representation. Our legal argument mining labels are Issue, Reason, Conclusion, and Non-IRC classes. We evaluate the interaction between different context configurations (pre-, post-, and bidirectional) and label diversity under various few-shot settings on a legal case summary annotation task. Results show that our combined approach outperforms traditional retrieval strategies across multiple large language models with improved macro F1 scores. This work demonstrates a way of improving legal argument mining in low-resource settings by optimizing the selection of few-shot examples.<\/jats:p>","DOI":"10.3233\/faia251590","type":"book-chapter","created":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T12:04:58Z","timestamp":1764849898000},"source":"Crossref","is-referenced-by-count":0,"title":["Label-Aware Contextual Retrieval for Few-Shot Legal Argument Mining"],"prefix":"10.3233","author":[{"given":"Huihui","family":"Xu","sequence":"first","affiliation":[{"name":"Intelligent Systems Program, University of Pittsburgh"},{"name":"Learning Research and Development Center, University of Pittsburgh"}]},{"given":"Kevin","family":"Ashley","sequence":"additional","affiliation":[{"name":"Intelligent Systems Program, University of Pittsburgh"},{"name":"Learning Research and Development Center, University of Pittsburgh"},{"name":"School of Law, University of Pittsburgh"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Legal Knowledge and Information Systems"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251590","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T12:04:58Z","timestamp":1764849898000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251590"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,2]]},"ISBN":["9781643686387"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251590","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,2]]}}}