{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T17:21:43Z","timestamp":1774632103365,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":26,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819730759","type":"print"},{"value":"9789819730766","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-97-3076-6_12","type":"book-chapter","created":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T23:02:21Z","timestamp":1716850941000},"page":"167-182","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Pushing the\u00a0Boundaries of\u00a0Legal Information Processing with\u00a0Integration of\u00a0Large Language Models"],"prefix":"10.1007","author":[{"given":"Chau","family":"Nguyen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thanh","family":"Tran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khang","family":"Le","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hien","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Truong","family":"Do","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Trang","family":"Pham","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Son T.","family":"Luu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Trung","family":"Vo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Le-Minh","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,28]]},"reference":[{"key":"12_CR1","doi-asserted-by":"crossref","unstructured":"Nguyen, C., Le, N.-K., Nguyen, D.-H., Nguyen, P., Nguyen, L.-M.: A legal information retrieval system for statute law. In: Proceedings of ACIIDS (2022)","DOI":"10.1007\/978-981-19-8234-7_29"},{"key":"12_CR2","doi-asserted-by":"crossref","unstructured":"Goebel, R., Kano, Y., Kim, M.-Y., Rabelo, J., Satoh, K., Yoshioka, M.: Summary of the competition on legal information, extraction\/entailment (COLIEE) 2023. In: Proceedings of ICAIL, pp.\u00a0472\u2013480 (2023)","DOI":"10.1145\/3594536.3595176"},{"issue":"1","key":"12_CR3","first-page":"20","volume":"28","author":"AP Dawid","year":"1979","unstructured":"Dawid, A.P., Skene, A.M.: Maximum likelihood estimation of observer error-rates using the EM algorithm. J. Roy. Stat. Soc.: Ser. C (Appl. Stat.) 28(1), 20\u201328 (1979)","journal-title":"J. Roy. Stat. Soc.: Ser. C (Appl. Stat.)"},{"key":"12_CR4","unstructured":"Li, H., Su, W., Wang, C., Wu, Y., Ai, Q., Liu, Y.: Thuir@ COLIEE 2023: incorporating structural knowledge into pre-trained language models for legal case retrieval. In: COLIEE 2023 (2023)"},{"key":"12_CR5","doi-asserted-by":"crossref","unstructured":"Vuong, T.-H.-Y., Nguyen, H.-L., Nguyen, T.-M., Nguyen, H.-T., Nguyen, T.-B., Nguyen, H.-T.: NOWJ at COLIEE 2023\u2013multi-task and ensemble approaches in legal information processing. In: COLIEE 2023 (2023)","DOI":"10.1007\/s12626-024-00157-3"},{"key":"12_CR6","unstructured":"Bui, M.Q., et al.: JNLP @COLIEE-2023: data augmentation and large language model for legal case retrieval and entailment. In: COLIEE 2023 (2023)"},{"key":"12_CR7","unstructured":"Nguyen, C., et al.: Captain at COLIEE 2023: efficient methods for legal information retrieval and entailment tasks. In: COLIEE 2023 (2024)"},{"key":"12_CR8","unstructured":"Li, H., Wang, C., Su, W., Wu, Y., Ai, Q., Liu, Y.: THUIR@ COLIEE 2023: more parameters and legal knowledge for legal case entailment. arXiv preprint arXiv:2305.06817 (2023)"},{"key":"12_CR9","unstructured":"Masaharu, Y., Yasuhiro, A.: HUKB at COLIEE 2023: statute law task. In: COLIEE 2023 (2024)"},{"key":"12_CR10","unstructured":"Onaga, T., Fujita, M., Kano, Y.: Japanese legal bar problem solver focusing on person names. In: Workshop of the COLIEE 2023 in the 19th International Conference on Artificial Intelligence and Law (ICAIL) (2023)"},{"key":"12_CR11","unstructured":"Rabelo, J., Kim, M.-Y., Goebel, R.: HUKB at COLIEE 2023: statute law task. In: COLIEE 2023 (2024)"},{"key":"12_CR12","unstructured":"Nguyen, H.-T., et al.: JNLP team: deep learning for legal processing in COLIEE 2020. In: COLIEE 2020 (2020)"},{"key":"12_CR13","unstructured":"Nguyen, H.-T., et al.: JNLP team: deep learning approaches for legal processing tasks in COLIEE 2021. arXiv preprint arXiv:2106.13405 (2021)"},{"key":"12_CR14","doi-asserted-by":"crossref","unstructured":"Ma, X., Wang, L., Yang, N., Wei, F., Lin, J.: Fine-tuning llama for multi-stage text retrieval. arXiv preprint arXiv:2310.08319 (2023)","DOI":"10.1145\/3626772.3657951"},{"key":"12_CR15","unstructured":"Touvron, H., et al.: et\u00a0al.: Llama 2: open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023)"},{"key":"12_CR16","unstructured":"Brown, T.B., et al.: et al.: Language models are few-shot learners. arXiv preprint arXiv:2005.14165 (2020)"},{"issue":"9","key":"12_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3560815","volume":"55","author":"P Liu","year":"2023","unstructured":"Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., Neubig, G.: Pre-train, prompt, and predict: a systematic survey of prompting methods in natural language processing. ACM Comput. Surv. 55(9), 1\u201335 (2023)","journal-title":"ACM Comput. Surv."},{"key":"12_CR18","doi-asserted-by":"crossref","unstructured":"Reimers, N., Gurevych, I.: Sentence-BERT: sentence embeddings using Siamese BERT-networks. In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) Proceedings of EMNLP-IJCNLP, pp.\u00a03982\u20133992 (2019)","DOI":"10.18653\/v1\/D19-1410"},{"key":"12_CR19","unstructured":"Jiang, A.Q., et\u00a0al.: Mistral 7b. arXiv preprint arXiv:2310.06825 (2023)"},{"key":"12_CR20","unstructured":"Kojima, T., Gu, S.S., Reid, M., Matsuo, Y., Iwasawa, Y.: Large language models are zero-shot reasoners. arXiv preprint arXiv:2205.11916 (2022)"},{"key":"12_CR21","doi-asserted-by":"crossref","unstructured":"Reynolds, L., McDonell, K.: Prompt programming for large language models: beyond the few-shot paradigm. In: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, pp.\u00a01\u20137 (2021)","DOI":"10.1145\/3411763.3451760"},{"key":"12_CR22","doi-asserted-by":"crossref","unstructured":"Bach, S., et al.: PromptSource: an integrated development environment and repository for natural language prompts. In: ACL: System Demonstrations (2022)","DOI":"10.18653\/v1\/2022.acl-demo.9"},{"key":"12_CR23","doi-asserted-by":"crossref","unstructured":"Niklaus, J., Matoshi, V., Sturmer, M., Chalkidis, I., Ho, D.E.: MultiLegalPile: a 689GB multilingual legal corpus. arXiv, vol.\u00a0abs\/2306.02069 (2023)","DOI":"10.18653\/v1\/2024.acl-long.805"},{"key":"12_CR24","doi-asserted-by":"crossref","unstructured":"Chalkidis, I., Garneau, N., Goanta, C., Katz, D.M., S\u00f8gaard, A.: LeXFiles and LegalLAMA: facilitating English multinational legal language model development. In: ACL, Toronto, Canada (2023)","DOI":"10.18653\/v1\/2023.acl-long.865"},{"key":"12_CR25","doi-asserted-by":"crossref","unstructured":"Nogueira, R., Jiang, Z., Pradeep, R., Lin, J.: Document ranking with a pretrained sequence-to-sequence model. In: Findings of EMNLP, Online (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.63"},{"key":"12_CR26","unstructured":"Nguyen, C., Nguyen, L.-M.: Employing label models on ChatGPT answers improves legal text entailment performance. Lecture Notes in Artificial Intelligence (2023)"}],"container-title":["Lecture Notes in Computer Science","New Frontiers in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-3076-6_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T04:01:04Z","timestamp":1732075264000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-3076-6_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819730759","9789819730766"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-3076-6_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"28 May 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"JSAI-isAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"JSAI International Symposium on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hamamatsu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 May 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 May 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"jsai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.ai-gakkai.or.jp\/isai\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}