@inproceedings{tan-minh-etal-2024-enhancing,
title = "Enhancing Legal Violation Identification with {LLM}s and Deep Learning Techniques: Achievements in the {L}egal{L}ens 2024 Competition",
author = "Tan Minh, Nguyen and
Ngoc Mai, Duy and
Xuan Bach, Le and
Huu Dung, Nguyen and
Cong Minh, Pham and
Nguyen, Ha Thanh and
Vuong, Thi Hai Yen",
editor = "Aletras, Nikolaos and
Chalkidis, Ilias and
Barrett, Leslie and
Goan{\textcommabelow{t}}{\u{a}}, C{\u{a}}t{\u{a}}lina and
Preo{\textcommabelow{t}}iuc-Pietro, Daniel and
Spanakis, Gerasimos",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2024",
month = nov,
year = "2024",
address = "Miami, FL, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.nllp-1.28",
pages = "336--345",
abstract = "LegalLens is a competition organized to encourage advancements in automatically detecting legal violations. This paper presents our solutions for two tasks Legal Named Entity Recognition (L-NER) and Legal Natural Language Inference (L-NLI). Our approach involves fine-tuning BERT-based models, designing methods based on data characteristics, and a novel prompting template for data augmentation using LLMs. As a result, we secured first place in L-NER and third place in L-NLI among thirty-six participants. We also perform error analysis to provide valuable insights and pave the way for future enhancements in legal NLP. Our implementation is available at https://github.com/lxbach10012004/legal-lens/tree/main",
}
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<abstract>LegalLens is a competition organized to encourage advancements in automatically detecting legal violations. This paper presents our solutions for two tasks Legal Named Entity Recognition (L-NER) and Legal Natural Language Inference (L-NLI). Our approach involves fine-tuning BERT-based models, designing methods based on data characteristics, and a novel prompting template for data augmentation using LLMs. As a result, we secured first place in L-NER and third place in L-NLI among thirty-six participants. We also perform error analysis to provide valuable insights and pave the way for future enhancements in legal NLP. Our implementation is available at https://github.com/lxbach10012004/legal-lens/tree/main</abstract>
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%0 Conference Proceedings
%T Enhancing Legal Violation Identification with LLMs and Deep Learning Techniques: Achievements in the LegalLens 2024 Competition
%A Tan Minh, Nguyen
%A Ngoc Mai, Duy
%A Xuan Bach, Le
%A Huu Dung, Nguyen
%A Cong Minh, Pham
%A Nguyen, Ha Thanh
%A Vuong, Thi Hai Yen
%Y Aletras, Nikolaos
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Goan\textcommabelowtă, Cătălina
%Y Preo\textcommabelowtiuc-Pietro, Daniel
%Y Spanakis, Gerasimos
%S Proceedings of the Natural Legal Language Processing Workshop 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, FL, USA
%F tan-minh-etal-2024-enhancing
%X LegalLens is a competition organized to encourage advancements in automatically detecting legal violations. This paper presents our solutions for two tasks Legal Named Entity Recognition (L-NER) and Legal Natural Language Inference (L-NLI). Our approach involves fine-tuning BERT-based models, designing methods based on data characteristics, and a novel prompting template for data augmentation using LLMs. As a result, we secured first place in L-NER and third place in L-NLI among thirty-six participants. We also perform error analysis to provide valuable insights and pave the way for future enhancements in legal NLP. Our implementation is available at https://github.com/lxbach10012004/legal-lens/tree/main
%U https://aclanthology.org/2024.nllp-1.28
%P 336-345
Markdown (Informal)
[Enhancing Legal Violation Identification with LLMs and Deep Learning Techniques: Achievements in the LegalLens 2024 Competition](https://aclanthology.org/2024.nllp-1.28) (Tan Minh et al., NLLP 2024)
ACL
- Nguyen Tan Minh, Duy Ngoc Mai, Le Xuan Bach, Nguyen Huu Dung, Pham Cong Minh, Ha Thanh Nguyen, and Thi Hai Yen Vuong. 2024. Enhancing Legal Violation Identification with LLMs and Deep Learning Techniques: Achievements in the LegalLens 2024 Competition. In Proceedings of the Natural Legal Language Processing Workshop 2024, pages 336–345, Miami, FL, USA. Association for Computational Linguistics.