| [1] |
Devlin J, Chang MW, Lee K, Toutanova K. 2019. BERT: pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), eds. Burstein J, Doran C, Solorio T. Minneapolis, Minnesota: Association for Computational Linguistics. pp. 4171−4186 doi: 10.18653/v1/N19-1423 |
| [2] |
Liu Y, Ott M, Goyal N, Du J, Joshi M, et al. 2019. RoBERTa: a robustly optimized BERT pretraining approach. |
| [3] |
Chalkidis I, Fergadiotis M, Malakasiotis P, Aletras N, Androutsopoulos I. 2020. LEGAL-BERT: the muppets straight out of law school. In Findings of the Association for Computational Linguistics: EMNLP 2020, eds. Cohn T, He Y, Liu Y. Minneapolis, Minnesota: Association for Computational Linguistics. pp. 2898−2904 doi: 10.18653/v1/2020.findings-emnlp.261 |
| [4] |
Gao C, Xiao C, Liu Z, Chen H, Liu Z, et al. 2024. Enhancing legal case retrieval via scaling high-quality synthetic query-candidate pairs. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, eds. Al-Onaizan Y, Bansal M, Chen YN. Miami, Florida, USA: Association for Computational Linguistics. pp. 7086−7100 doi: 10.18653/v1/2024.emnlp-main.402 |
| [5] |
Karpukhin V, Oguz B, Min S, Lewis P, Wu L, et al. 2020. Dense passage retrieval for open-domain question answering. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), eds. Webber B, Cohn T, He Y, Liu Y. Online: Association for Computational Linguistics. pp. 6769−6781 doi: 10.18653/v1/2020.emnlp-main.550 |
| [6] |
Cui J, Ning M, Li Z, Chen B, Yan Y, et al. 2023. Chatlaw: a multi-agent legal assistant based on a role-aligned mixture-of-experts architecture. |
| [7] |
Cui J, Ning M, Li Z, Li H, Ya Y, et al. 2026. Chatlaw: a multi-agent legal assistant based on a role-aligned mixture-of-experts architecture. |
| [8] |
Guha N, Nyarko J, Ho D, Ré C, Chilton A, et al. 2023. LegalBench: a collaboratively built benchmark for measuring legal reasoning in large language models. In Advances in Neural Information Processing Systems 36. USA: Neural Information Processing Systems Foundation, Inc. (NeurIPS). pp. 44123−44279 doi: 10.52202/075280-1915 |
| [9] |
Yao S, Ke Q, Wang Q, Li K, Hu J. 2024. Lawyer GPT: a legal large language model with enhanced domain knowledge and reasoning capabilities. Proceedings of the 2024 3rd International Symposium on Robotics, Artificial Intelligence and Information Engineering, Singapore, 5−7 July, 2024. New York, NY, USA: Association for Computing Machinery. pp. 108−112 doi: 10.1145/3689299.3689319 |
| [10] |
Vuong YTH, Bui QM, Nguyen HT, Nguyen TTT, Tran V, et al. 2023. SM-BERT-CR: a deep learning approach for case law retrieval with supporting model. |
| [11] |
Nguyen HT, Phi MK, Ngo XB, Tran V, Nguyen LM, et al. 2022. Attentive deep neural networks for legal document retrieval. |
| [12] |
Beltagy I, Peters ME, Cohan A. 2020. Longformer: the long-document transformer. |
| [13] |
Ma Y, Wu Y, Su W, Ai Q, Liu Y. 2023. CaseEncoder: a knowledge-enhanced pre-trained model for legal case encoding. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, eds. Bouamor H, Pino J, Bali K. Singapore: Association for Computational Linguistics. pp. 7134−7143 doi: 10.18653/v1/2023.emnlp-main.441 |
| [14] |
Bhattacharya P, Ghosh K, Pal A, Ghosh S. 2022. Legal case document similarity: you need both network and text. |
| [15] |
Tang Y, Qiu R, Liu Y, Li X, Huang Z. 2024. CaseGNN: graph neural networks for legal case retrieval with text-attributed graphs. Advances in Information Retrieval, eds. Goharian N, Tonellotto N, He Y, Lipani A, McDonald G, et al. Cham: Springer. pp. 80−95 doi: 10.1007/978-3-031-56060-6_6 |
| [16] |
Tang Y, Qiu R, Yin H, Li X, Huang Z. 2024. CaseLink: inductive graph learning for legal case retrieval. Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, Washington DC, USA 14−18 July, 2024. New York, NY, USA: Association for Computing Machinery. pp. 2199−2209 doi: 10.1145/3626772.3657693 |
| [17] |
Ma Y, Wu Y, Ai Q, Liu Y, Shao Y, et al. 2023. Incorporating structural information into legal case retrieval. |
| [18] |
Li H, Ai Q, Chen J, Dong Q, Wu Y, et al. 2023. SAILER: structure-aware pre-trained language model for legal case retrieval. Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, Taipei, Taiwan, China, 23−27 July, 2023. New York, NY, USA: Association for Computing Machinery. pp. 1035−1044 doi: 10.1145/3539618.3591761 |
| [19] |
Bi S, Ali Z, Wang M, Wu T, Qi G. 2022. Learning heterogeneous graph embedding for Chinese legal document similarity. |
| [20] |
Zhang Y, Zhai S, Meng Y, Bi S, Chen Y, et al. 2024. Event is more valuable than you think: improving the Similar Legal Case Retrieval via event knowledge. |
| [21] |
Sun Z, Xu J, Zhang X, Dong Z, Wen JR. 2023. Law article-enhanced legal case matching: a causal learning approach. Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, Taipei, Taiwan, China, 23−27 July, 2023. New York, NY, USA: Association for Computing Machinery. pp. 1549−1558 doi: 10.1145/3539618.3591709 |
| [22] |
Zhang K, Chen C, Wang Y, Tian Q, Bai L. 2023. CFGL-LCR: a counterfactual graph learning framework for legal case retrieval. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Long Beach CA, USA, 6−10 August, 2023. New York, NY, USA: Association for Computing Machinery. pp. 3332−3341 doi: 10.1145/3580305.3599273 |
| [23] |
Wang Z. 2022. Legal element-oriented modeling with multi-view contrastive learning for legal case retrieval. 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy, 18−23 July, 2022. USA: IEEE. pp. 1−10 doi: 10.1109/IJCNN55064.2022.9892487 |
| [24] |
Sarmah B, Mehta D, Hall B, Rao R, Patel S, et al. 2024. HybridRAG: integrating knowledge graphs and vector retrieval augmented generation for efficient information extraction. Proceedings of the 5th ACM International Conference on AI in Finance, Brooklyn NY, USA, 14−17 November, 2024. New York, NY, USA: Association for Computing Machinery. pp. 608−616 doi: 10.1145/3677052.3698671 |
| [25] |
Kalra R, Wu Z, Gulley A, Hilliard A, Guan X, et al. 2024. HyPA-RAG: a hybrid parameter adaptive retrieval-augmented generation system for AI legal and policy applications. Proceedings of the 1st Workshop on Customizable NLP: Progress and Challenges in Customizing NLP for a Domain, Application, Group, or Individual (CustomNLP4U), eds. Kumar S, Balachandran V, Park CY, Shi W, Hayati SA, et al. Miami, Florida, USA: Association for Computational Linguistics. pp. 237−256 doi: 10.18653/v1/2024.customnlp4u-1.18 |
| [26] |
Wiratunga N, Abeyratne R, Jayawardena L, Martin K, Massie S, et al. 2024. CBR-RAG: Case-Based Reasoning for Retrieval Augmented Generation in LLMs for Legal Question Answering. In Case-Based Reasoning Research and Development, eds. Recio-Garcia JA, Orozco-del-Castillo MG, Bridge D. Cham: Springer Nature Switzerland. pp. 445−460 doi: 10.1007/978-3-031-63646-2_29 |
| [27] |
Schlichtkrull M, Kipf TN, Bloem P, van den Berg R, Titov I, et al. 2018. Modeling relational data with graph convolutional networks. In The Semantic Web, eds. Gangemi A, Navigli R, Vidal ME, Hitzler P, Troncy R, et al. Cham: Springer. pp. 593−607 doi: 10.1007/978-3-319-93417-4_38 |
| [28] |
Ma Y, Shao Y, Wu Y, Liu Y, Zhang R, et al. 2021. LeCaRD: a legal case retrieval dataset for Chinese law system. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, 11−15 July, 2021. New York, NY, USA: Association for Computing Machinery. pp. 2342−2348 doi: 10.1145/3404835.3463250 |