CaseEncoder: A Knowledge-enhanced Pre-trained Model for Legal Case Encoding (2023.emnlp-main)
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| Challenge: | Existing legal-oriented PLMs rely on replacing general domain training data with legal data or extending the input length to fit the long-length characteristic of legal data. |
| Approach: | They propose a legal document encoder that leverages fine-grained legal knowledge in both the data sampling and pre-training phases. |
| Outcome: | The proposed model outperforms existing general domain pre-training models and legal-specific pre-trainers on multiple benchmarks. |
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| Challenge: | Prior case retrieval datasets do not simulate a realistic setting because they use complete case documents while only masking references to prior cases. |
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| Challenge: | Existing methods for legal case retrieval lack the definition of relevance for legal cases . however, the definition goes beyond the common semantic relevance of ad-hoc retrieval. |
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CLERC: A Dataset for U. S. Legal Case Retrieval and Retrieval-Augmented Analysis Generation (2025.findings-naacl)
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Abe Bohan Hou, Orion Weller, Guanghui Qin, Eugene Yang, Dawn Lawrie, Nils Holzenberger, Andrew Blair-Stanek, Benjamin Van Durme
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Learning Interpretable Legal Case Retrieval via Knowledge-Guided Case Reformulation (2024.emnlp-main)
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| Challenge: | Existing methods for legal case retrieval often overlook the incorporation of legal expert knowledge, leading to unsatisfactory retrieval performance. |
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| Challenge: | Legal Judgement Prediction systems without expert-informed adjustments can be vulnerable to shallow, distracting surface signals. |
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