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Survey on the legal question answering problem
Zapiski Nauchnykh Seminarov POMI, Investigations on applied mathematics and informatics. Part IV, Tome 540 (2024), pp. 194-213 Cet article a éte moissonné depuis la source Math-Net.Ru

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Recent advances in multi-document summarization in the legal domain have demonstrated significant progress in the extraction and compression of information from legal texts. Current methods utilize a combination of natural language processing, machine learning, and data mining techniques to identify and distill key elements and themes from a multitude of legal documents. This process creates structured, concise, and relevant summaries based on specific legal queries or topics, often referred to as multi-document abstracts. These abstracts facilitate a more efficient review by capturing the essence of complex and voluminous legal materials without losing the necessary detail. The focus of recent research has been on enhancing the accuracy of information retrieval, improving the coherence of the generated summaries, and ensuring the relevacy of the content to the specific legal issue at hand. Although challenges remain, particularly in the nuances of legal language and the diversity of document types, the trajectory of the field is toward more sophisticated and user-friendly systems that promise to transform the landscape of legal research and information accessibility. 
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A. Sabieva; A. Zhamankhan; N. Zhetessov; A. Kubayeva; I. Akhmetov; A. Pak; D. Akhmetova; A. Zhaxylykova; A. Yelenov. Survey on the legal question answering problem. Zapiski Nauchnykh Seminarov POMI, Investigations on applied mathematics and informatics. Part IV, Tome 540 (2024), pp. 194-213. http://geodesic.mathdoc.fr/item/ZNSL_2024_540_a10/

[1] I. Akhmetov and A. Pak, “TSP review: performance comparison of the well-known methods on a standardized dataset”, Proc. 2023 19th Int. Asian Sch.-Semin. Optim. Probl. Complex Syst. (OPCS), 2023, 4–9 | DOI

[2] A. Ravichander, A.W. Black, S. Wilson, T. Norton, and N. Sadeh, “Question answering for privacy policies: Combining computational and legal perspectives”, Proc. 2019 Conf. Empir. Methods Nat. Lang. Process. Int. Joint Conf. Nat. Lang. Process. (EMNLP-IJCNLP), 2019, 4947–4958

[3] P. Wang, L. Li, L. Chen, Z. Cai, D. Zhu, B. Lin, Y. Cao, Q. Liu, T. Liu, and Z. Sui, Large Language Models are not Fair Evaluators, 2023, arXiv: 2305.17926

[4] A. Louis, G. van Dijck, and G. Spanakis, Interpretable Long-Form Legal Question Answering with Retrieval-Augmented Large Language Models, 2023, arXiv: 2309.17050

[5] K. Krishna, A. Roy, and M. Iyyer, Hurdles to Progress in Long-form Question Answering, 2021, arXiv: 2103.06332

[6] S. Wang, F. Xu, L. Thompson, E. Choi, and M. Iyyer, “Modeling Exemplification in Long-form Question Answering via Retrieval”, Proc. 2022 Conf. North Am. Chapter Assoc. Comput. Linguist.: Hum. Lang. Technol., 2022, 2079–2092 | DOI

[7] F. Xu, Y. Song, M. Iyyer, and E. Choi, A Critical Evaluation of Evaluations for Long-form Question Answering, 2023, arXiv: 2305.18201

[8] J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F.L. Aleman, et al., GPT-4 Technical Report, 2023, arXiv: 2303.08774

[9] Z. Ji, N. Lee, R. Frieske, T. Yu, D. Su, Y. Xu, E. Ishii, Y.J. Bang, A. Madotto, and P. Fung, “Survey of Hallucination in Natural Language Generation”, ACM Comput. Surv., 55:12 (2023), 248, 38 pp.

[10] P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W.-t. Yih, T. Rocktäschel, S. Riedel, and D. Kiela, Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, 2021, arXiv: 2005.11401

[11] G. Mialon, R. Dessì, M. Lomeli, C. Nalmpantis, R. Pasunuru, R. Raileanu, B. Rozière, T. Schick, J. Dwivedi-Yu, A. Celikyilmaz, E. Grave, Y. LeCun, and T. Scialom, Augmented Language Models: a Survey, 2023, arXiv: 2302.07842

[12] A. Lazaridou, E. Gribovskaya, W. Stokowiec, and N. Grigorev, Internet-augmented language models through few-shot prompting for open-domain question answering, 2022, arXiv: 2203.05115

[13] J. Saad-Falcon, O. Khattab, C. Potts, and M. Zaharia, ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems, 2023, arXiv: 2311.09476

[14] H. Li, Y. Su, D. Cai, Y. Wang, and L. Liu, A Survey on Retrieval-Augmented Text Generation, 2022, arXiv: 2202.01110 | MR

[15] A. Asai, S. Min, Z. Zhong, and D. Chen, “Retrieval-based Language Models and Applications”, Proc. 61st Annu. Meet. Assoc. Comput. Linguist., 2023, 41–46

[16] Y. Gao, Y. Xiong, X. Gao, K. Jia, J. Pan, Y. Bi, Y. Dai, J. Sun, Q. Guo, M. Wang, and H. Wang, Retrieval-Augmented Generation for Large Language Models: A Survey, 2024, arXiv: 2312.10997

[17] S.M. Gerrish and D.M. Blei, “Predicting legislative roll calls from text”, Proc. 28th Int. Conf. Mach. Learn., 2011, 489–496

[18] T. Yano, N.A. Smith, and J.D. Wilkerson, “Textual Predictors of Bill Survival in Congressional Committees”, Proc. 2012 Conf. North Am. Chapter Assoc. Comput. Linguist.: Hum. Lang. Technol., 2012, 793–802

[19] M. Kim, Y. Xu, and R. Goebel, “Summarization of Legal Texts with High Cohesion and Automatic Compression Rate”, New Front. Artif. Intell., 2013, 190–204 | DOI

[20] A. Kali, P. Shamoi, Y. Zhangbyrbayev, and A. Zhandaulet, “Computing with Words for Industrial Applications”, Intell. Syst. Appl., 2023, 257–271

[21] P. Shamoi and A. Inoue, “Computing with Words for Direct Marketing Support System”, Proc. Midwest Artif. Intell. Cogn. Sci. Conf., 2012

[22] J.B. Ruhl, D.M. Katz, and M.J. Bommarito, “Harnessing legal complexity”, Science, 355:6332 (2017), 1377–1378 | DOI

[23] C.-N. Chau, T.-S. Nguyen, and L.-M. Nguyen, “VNLawBERT: A Vietnamese Legal Answer Selection Approach Using BERT Language Model”, Proc. 2020 7th NAFOSTED Conf. Inf. Comput. Sci., 2020, 298–301