Related papers: How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence

How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence

URL: http://arxiv.org/abs/2004.12158v5
Date: Mon, 18 May 2020 06:32:27 GMT
Title: How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence
Authors: Haoxi Zhong, Chaojun Xiao, Cunchao Tu, Tianyang Zhang, Zhiyuan Liu, Maosong Sun
Abstract summary: Legal Artificial Intelligence (LegalAI) focuses on applying the technology of artificial intelligence, especially natural language processing, to benefit tasks in the legal domain. This paper introduces the history, the current state, and the future directions of research in LegalAI.
Score: 81.04070052740596
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Legal Artificial Intelligence (LegalAI) focuses on applying the technology of artificial intelligence, especially natural language processing, to benefit tasks in the legal domain. In recent years, LegalAI has drawn increasing attention rapidly from both AI researchers and legal professionals, as LegalAI is beneficial to the legal system for liberating legal professionals from a maze of paperwork. Legal professionals often think about how to solve tasks from rule-based and symbol-based methods, while NLP researchers concentrate more on data-driven and embedding methods. In this paper, we introduce the history, the current state, and the future directions of research in LegalAI. We illustrate the tasks from the perspectives of legal professionals and NLP researchers and show several representative applications in LegalAI. We conduct experiments and provide an in-depth analysis of the advantages and disadvantages of existing works to explore possible future directions. You can find the implementation of our work from https://github.com/thunlp/CLAIM.

Related papers

InternLM-Law: An Open Source Chinese Legal Large Language Model [72.2589401309848]
InternLM-Law is a specialized LLM tailored for addressing diverse legal queries related to Chinese laws. We meticulously construct a dataset in the Chinese legal domain, encompassing over 1 million queries. InternLM-Law achieves the highest average performance on LawBench, outperforming state-of-the-art models, including GPT-4, on 13 out of 20 subtasks.
arXiv Detail & Related papers (2024-06-21T06:19:03Z)
Towards A Structured Overview of Use Cases for Natural Language Processing in the Legal Domain: A German Perspective [43.662441393491584]
In recent years, the field of Legal Tech has risen in prevalence, as the Natural Language Processing (NLP) and legal disciplines have combined forces to digitalize legal processes. In this work, we aim to build a structured overview of Legal Tech use cases, grounded in NLP literature, but also supplemented by voices from legal practice in Germany.
arXiv Detail & Related papers (2024-04-29T14:56:47Z)
Promises and pitfalls of artificial intelligence for legal applications [19.8511844390731]
We argue that this claim is not supported by the current evidence. We dive into AI's increasingly prevalent roles in three types of legal tasks. We make recommendations for better evaluation and deployment of AI in legal contexts.
arXiv Detail & Related papers (2024-01-10T19:50:37Z)
The Ethics of Automating Legal Actors [58.81546227716182]
We argue that automating the role of the judge raises difficult ethical challenges, in particular for common law legal systems. Our argument follows from the social role of the judge in actively shaping the law, rather than merely applying it. Even in the case the models could achieve human-level capabilities, there would still be remaining ethical concerns inherent in the automation of the legal process.
arXiv Detail & Related papers (2023-12-01T13:48:46Z)
Large Language Models in Law: A Survey [34.785207813971134]
The application of legal large language models (LLMs) is still in its nascent stage. We provide an overview of AI technologies in the legal field and showcase the recent research in LLMs. We explore the limitations of legal LLMs, including data, algorithms, and judicial practice.
arXiv Detail & Related papers (2023-11-26T00:48:12Z)
ChatGPT and Works Scholarly: Best Practices and Legal Pitfalls in Writing with AI [9.550238260901121]
We offer approaches to evaluating whether or not such AI-writing violates copyright or falls within the safe harbor of fair use. As AI is likely to grow more capable in the coming years, it is appropriate to begin integrating AI into scholarly writing activities.
arXiv Detail & Related papers (2023-05-04T15:38:20Z)
An Uncommon Task: Participatory Design in Legal AI [64.54460979588075]
We examine a notable yet understudied AI design process in the legal domain that took place over a decade ago. We show how an interactive simulation methodology allowed computer scientists and lawyers to become co-designers.
arXiv Detail & Related papers (2022-03-08T15:46:52Z)
Lawformer: A Pre-trained Language Model for Chinese Legal Long Documents [56.40163943394202]
We release the Longformer-based pre-trained language model, named as Lawformer, for Chinese legal long documents understanding. We evaluate Lawformer on a variety of LegalAI tasks, including judgment prediction, similar case retrieval, legal reading comprehension, and legal question answering.
arXiv Detail & Related papers (2021-05-09T09:39:25Z)
AI and Legal Argumentation: Aligning the Autonomous Levels of AI Legal Reasoning [0.0]
Legal argumentation is a vital cornerstone of justice, underpinning an adversarial form of law. Extensive research has attempted to augment or undertake legal argumentation via the use of computer-based automation including Artificial Intelligence (AI) An innovative meta-approach is proposed to apply the Levels of Autonomy (LoA) of AI Legal Reasoning to the maturation of AI and Legal Argumentation (AILA)
arXiv Detail & Related papers (2020-09-11T22:05:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.