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相关论文: Legal Evalutions and Challenges of Large Language …

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Large language models (LLMs) are being increasingly integrated into legal applications, including judicial decision support, legal practice assistance, and public-facing legal services. While LLMs show strong potential in handling legal…

Better understanding of Large Language Models' (LLMs) legal analysis abilities can contribute to improving the efficiency of legal services, governing artificial intelligence, and leveraging LLMs to identify inconsistencies in law. This…

This chapter explores the application of Large Language Models in the legal domain, showcasing their potential to optimise and augment traditional legal tasks by analysing possible use cases, such as assisting in interpreting statutes,…

计算与语言 · 计算机科学 2025-12-12 Simone Corbo

Large Language Models (LLMs) have significantly advanced the development of Legal Artificial Intelligence (Legal AI) in recent years, enhancing the efficiency and accuracy of legal tasks. To advance research and applications of LLM-based…

计算与语言 · 计算机科学 2025-09-15 Zhitian Hou , Zihan Ye , Nanli Zeng , Tianyong Hao , Kun Zeng

Recent advances in test-time scaling of large language models (LLMs), exemplified by DeepSeek-R1 and OpenAI's o1, show that extending the chain of thought during inference can significantly improve general reasoning performance. However,…

计算与语言 · 计算机科学 2025-11-11 Yinghao Hu , Yaoyao Yu , Leilei Gan , Bin Wei , Kun Kuang , Fei Wu

The advent of artificial intelligence (AI) has significantly impacted the traditional judicial industry. Moreover, recently, with the development of AI-generated content (AIGC), AI and law have found applications in various domains,…

计算与语言 · 计算机科学 2023-12-08 Jinqi Lai , Wensheng Gan , Jiayang Wu , Zhenlian Qi , Philip S. Yu

Large language models (LLMs) have transformed many fields, including natural language processing, computer vision, and reinforcement learning. These models have also made a significant impact in the field of law, where they are being…

计算与语言 · 计算机科学 2023-03-17 Zhongxiang Sun

The legal landscape encompasses a wide array of lawsuit types, presenting lawyers with challenges in delivering timely and accurate information to clients, particularly concerning critical aspects like potential imprisonment duration or…

人工智能 · 计算机科学 2024-07-30 Jia-Hong Huang , Chao-Chun Yang , Yixian Shen , Alessio M. Pacces , Evangelos Kanoulas

With the advancement of Artificial Intelligence (AI) and Large Language Models (LLMs), there is a profound transformation occurring in the realm of natural language processing tasks within the legal domain. The capabilities of LLMs are…

计算与语言 · 计算机科学 2024-04-02 Weicong Qin , Zhongxiang Sun

General and legal domain LLMs have demonstrated strong performance in various tasks of LegalAI. However, the current evaluations of these LLMs in LegalAI are defined by the experts of computer science, lacking consistency with the logic of…

计算与语言 · 计算机科学 2024-02-20 Yongfu Dai , Duanyu Feng , Jimin Huang , Haochen Jia , Qianqian Xie , Yifang Zhang , Weiguang Han , Wei Tian , Hao Wang

In an era dominated by Large Language Models (LLMs), understanding their capabilities and limitations, especially in high-stakes fields like law, is crucial. While LLMs such as Meta's LLaMA, OpenAI's ChatGPT, Google's Gemini, DeepSeek, and…

计算与语言 · 计算机科学 2025-09-29 Antreas Ioannou , Andreas Shiamishis , Nora Hollenstein , Nezihe Merve Gürel

Large language models (LLMs) have demonstrated great potential for domain-specific applications, such as the law domain. However, recent disputes over GPT-4's law evaluation raise questions concerning their performance in real-world legal…

计算与语言 · 计算机科学 2023-10-19 Ruihao Shui , Yixin Cao , Xiang Wang , Tat-Seng Chua

The performance of large language models (LLMs) has recently improved to the point where models can perform well on many language tasks. We show here that--for the first time--the models can also generate valid metalinguistic analyses of…

计算与语言 · 计算机科学 2025-07-14 Gašper Beguš , Maksymilian Dąbkowski , Ryan Rhodes

With the emergence of advanced reasoning models like OpenAI o3 and DeepSeek-R1, large language models (LLMs) have demonstrated remarkable reasoning capabilities. However, their ability to perform rigorous logical reasoning remains an open…

人工智能 · 计算机科学 2025-02-14 Hanmeng Liu , Zhizhang Fu , Mengru Ding , Ruoxi Ning , Chaoli Zhang , Xiaozhang Liu , Yue Zhang

A Large Language Model (LLM) represents a cutting-edge artificial intelligence model that generates coherent content, including grammatically precise sentences, human-like paragraphs, and syntactically accurate code snippets. LLMs can play…

软件工程 · 计算机科学 2023-12-11 Robson Santos , Italo Santos , Cleyton Magalhaes , Ronnie de Souza Santos

This paper investigates the mathematical reasoning capabilities of large language models (LLMs) using 50 newly constructed high-school-level word problems. Unlike prior studies that focus solely on answer correctness, we rigorously analyze…

人工智能 · 计算机科学 2025-02-24 Johan Boye , Birger Moell

Large language models (LLMs) have become central to modern AI workflows, powering applications from open-ended text generation to complex agent-based reasoning. However, debugging these models remains a persistent challenge due to their…

The use of Large Language Models (LLM) by providers of cybersecurity and digital infrastructures of all kinds is an ongoing development. It is suggested and on an experimental basis used to write the code for the systems, and potentially…

计算机与社会 · 计算机科学 2025-12-19 Kaspar Rosager Ludvigsen

The growing adoption of large language models in legal practice brings both significant promise and serious risk. Legal professionals stand to benefit from AI that can reason over contracts, draft documents, and analyze sources at scale,…

人工智能 · 计算机科学 2026-05-15 Olivia Peiyu Wang , Leilani H. Gilpin

While large language models (LLMs) have showcased impressive capabilities, they struggle with addressing legal queries due to the intricate complexities and specialized expertise required in the legal field. In this paper, we introduce…

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