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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

The use of Large Language Models (LLMs) in police operations is growing, yet an evaluation framework tailored to police operations remains absent. While LLM's responses may not always be legally incorrect, their unverified use still can…

计算与语言 · 计算机科学 2026-01-08 Sangyub Lee , Heedou Kim , Hyeoncheol Kim

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…

The ever-increasing workload of digital forensic labs raises concerns about law enforcement's ability to conduct both cyber-related and non-cyber-related investigations promptly. Consequently, this article explores the potential and…

密码学与安全 · 计算机科学 2025-02-03 Akila Wickramasekara , Frank Breitinger , Mark Scanlon

As large language models (LLMs) advance, concerns about their misconduct in complex social contexts intensify. Existing research overlooked the systematic understanding and assessment of their criminal capability in realistic interactions.…

密码学与安全 · 计算机科学 2025-10-20 Xinyi Wu , Geng Hong , Pei Chen , Yueyue Chen , Xudong Pan , Min Yang

While large language models (LLMs) present significant potential for supporting numerous real-world applications and delivering positive social impacts, they still face significant challenges in terms of the inherent risk of privacy…

人工智能 · 计算机科学 2025-01-17 Huandong Wang , Wenjie Fu , Yingzhou Tang , Zhilong Chen , Yuxi Huang , Jinghua Piao , Chen Gao , Fengli Xu , Tao Jiang , Yong Li

Polis is a platform that leverages machine intelligence to scale up deliberative processes. In this paper, we explore the opportunities and risks associated with applying Large Language Models (LLMs) towards challenges with facilitating,…

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

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

Spurred by the recent rapid increase in the development and distribution of large language models (LLMs) across industry and academia, much recent work has drawn attention to safety- and security-related threats and vulnerabilities of LLMs,…

计算与语言 · 计算机科学 2023-08-25 Maximilian Mozes , Xuanli He , Bennett Kleinberg , Lewis D. Griffin

With the continuous development of large language models (LLMs), transformer-based models have made groundbreaking advances in numerous natural language processing (NLP) tasks, leading to the emergence of a series of agents that use LLMs as…

人工智能 · 计算机科学 2024-11-15 Yuyou Gan , Yong Yang , Zhe Ma , Ping He , Rui Zeng , Yiming Wang , Qingming Li , Chunyi Zhou , Songze Li , Ting Wang , Yunjun Gao , Yingcai Wu , Shouling Ji

Large language models (LLMs) have significantly transformed the landscape of Natural Language Processing (NLP). Their impact extends across a diverse spectrum of tasks, revolutionizing how we approach language understanding and generations.…

密码学与安全 · 计算机科学 2025-06-13 Sara Abdali , Richard Anarfi , CJ Barberan , Jia He , Erfan Shayegani

Large language models (LLMs) represent significant breakthroughs in artificial intelligence and hold potential for applications within smart grids. However, as demonstrated in previous literature, AI technologies are susceptible to various…

密码学与安全 · 计算机科学 2025-04-22 Jiangnan Li , Yingyuan Yang , Jinyuan Sun

Many studies have demonstrated that large language models (LLMs) can produce harmful responses, exposing users to unexpected risks when LLMs are deployed. Previous studies have proposed comprehensive taxonomies of the risks posed by LLMs,…

计算与语言 · 计算机科学 2024-08-06 Yuxia Wang , Zenan Zhai , Haonan Li , Xudong Han , Lizhi Lin , Zhenxuan Zhang , Jingru Zhao , Preslav Nakov , Timothy Baldwin

Large language models (LLMs) have strong capabilities in solving diverse natural language processing tasks. However, the safety and security issues of LLM systems have become the major obstacle to their widespread application. Many studies…

Large Language Models (LLMs) have demonstrated extraordinary capabilities and contributed to multiple fields, such as generating and summarizing text, language translation, and question-answering. Nowadays, LLM is becoming a very popular…

计算与语言 · 计算机科学 2024-11-18 Badhan Chandra Das , M. Hadi Amini , Yanzhao Wu

Large language models (LLMs) are now deployed at unprecedented scale, assisting millions of users in daily tasks. However, the risk of these models assisting unlawful activities remains underexplored. In this study, we define this high-risk…

计算机与社会 · 计算机科学 2025-11-27 Xing Wang , Huiyuan Xie , Yiyan Wang , Chaojun Xiao , Huimin Chen , Holli Sargeant , Felix Steffek , Jie Shao , Zhiyuan Liu , Maosong Sun

Applying large language models (LLMs) to modern power systems presents a promising avenue for enhancing decision-making and operational efficiency. However, this action may also incur potential security threats, which have not been fully…

人工智能 · 计算机科学 2024-01-25 Jiaqi Ruan , Gaoqi Liang , Huan Zhao , Guolong Liu , Xianzhuo Sun , Jing Qiu , Zhao Xu , Fushuan Wen , Zhao Yang Dong

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
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