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相关论文: Evaluation of Large Language Models in Legal Appli…

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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

Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their…

Large Language Models (LLMs) have recently gained significant attention due to their remarkable capabilities in performing diverse tasks across various domains. However, a thorough evaluation of these models is crucial before deploying them…

Large language models (LLMs) have emerged as powerful tools with transformative potential across numerous domains, including healthcare and medicine. In the medical domain, LLMs hold promise for tasks ranging from clinical decision support…

计算与语言 · 计算机科学 2024-05-14 Xiaolan Chen , Jiayang Xiang , Shanfu Lu , Yexin Liu , Mingguang He , Danli Shi

Large language models (LLMs) have precipitated a dramatic improvement in the legal domain, yet the deployment of standalone models faces significant limitations regarding hallucination, outdated information, and verifiability. Recently, LLM…

计算机与社会 · 计算机科学 2026-01-13 Shuang Liu , Ruijia Zhang , Ruoyun Ma , Yujia Deng , Lanyi Zhu , Jiayu Li , Zelong Li , Zhibin Shen , Mengnan Du

In this paper, we review legal testing methods based on Large Language Models (LLMs), using the OPENAI o1 model as a case study to evaluate the performance of large models in applying legal provisions. We compare current state-of-the-art…

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

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

In recent years, the utilization of large language models for natural language dialogue has gained momentum, leading to their widespread adoption across various domains. However, their universal competence in addressing challenges specific…

计算与语言 · 计算机科学 2024-03-19 Ruizhe Zhang , Haitao Li , Yueyue Wu , Qingyao Ai , Yiqun Liu , Min Zhang , Shaoping Ma

Large Language Models (LLMs) represent an advanced evolution of earlier, simpler language models. They boast enhanced abilities to handle complex language patterns and generate coherent text, images, audios, and videos. Furthermore, they…

密码学与安全 · 计算机科学 2024-03-01 Jun Huang , Jiawei Zhang , Qi Wang , Weihong Han , Yanchun Zhang

This paper provides a comprehensive review of the current methods and metrics used to evaluate the performance of Large Language Models (LLMs) in code generation tasks. With the rapid growth in demand for automated software development,…

软件工程 · 计算机科学 2025-03-05 Liguo Chen , Qi Guo , Hongrui Jia , Zhengran Zeng , Xin Wang , Yijiang Xu , Jian Wu , Yidong Wang , Qing Gao , Jindong Wang , Wei Ye , Shikun Zhang

This paper presents a detailed case study examining the application of Large Language Models (LLMs) in the construction of test cases within the context of software engineering. LLMs, characterized by their advanced natural language…

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

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

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

Large Language Models (LLMs) have gained significant attention in the medical domain for their human-level capabilities, leading to increased efforts to explore their potential in various healthcare applications. However, despite such a…

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…

The deployment of large language models (LLMs) within the healthcare sector has sparked both enthusiasm and apprehension. These models exhibit the remarkable capability to provide proficient responses to free-text queries, demonstrating a…

计算与语言 · 计算机科学 2024-07-09 Zabir Al Nazi , Wei Peng

In the rapidly evolving domain of Natural Language Generation (NLG) evaluation, introducing Large Language Models (LLMs) has opened new avenues for assessing generated content quality, e.g., coherence, creativity, and context relevance.…

计算与语言 · 计算机科学 2024-06-13 Zhen Li , Xiaohan Xu , Tao Shen , Can Xu , Jia-Chen Gu , Yuxuan Lai , Chongyang Tao , Shuai Ma

Recent advances in large language models (LLMs) have unlocked novel opportunities for machine learning applications in the financial domain. These models have demonstrated remarkable capabilities in understanding context, processing vast…

综合金融 · 定量金融 2024-06-19 Yuqi Nie , Yaxuan Kong , Xiaowen Dong , John M. Mulvey , H. Vincent Poor , Qingsong Wen , Stefan Zohren
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