This study evaluates the performance of general-purpose AI, like ChatGPT, in legal question-answering tasks, highlighting significant risks to legal professionals and clients. It suggests leveraging foundational models enhanced by domain-specific knowledge to overcome these issues. The paper advocates for creating open-source legal AI systems to improve accuracy, transparency, and narrative diversity, addressing general AI's shortcomings in legal contexts.
Bridging Technology and Humanities: Evaluating the Impact of Large Language Models on Social Sciences Research with DeepSeek-R1
School of Physics and Information Technology, Shaanxi Normal University（陕西师范大学物理与信息技术学院）；School of Foreign Languages, Northwest University（西北大学外国语学院）；School of Computing, University of Georgia（佐治亚大学计算学院）
MoRSE: Bridging the Gap in Cybersecurity Expertise with Retrieval Augmented Generation
Istituto di Informatica e Telematica, Consiglio Nazionale Delle Ricerche（国家研究委员会信息与自动化研究所）；TeCIP, Scuola Universitaria Superiore Sant’Anna（圣安娜高等学校TeCIP研究所）；University of Padua（帕多瓦大学）；Delft University of Technology（代尔夫特理工大学）
WuYu-EnvLE-Bench: A Benchmark for Evaluating Large Language Models in Environmental Law Enforcement
School of Environment, Tsinghua University（清华大学环境学院）；College of Economics and Management, Beijing University of Technology（北京工业大学经济与管理学院）；State Key Laboratory of Iron and Steel Industry Environmental Protection, School of Environment, Tsinghua University（清华大学环境学院钢铁工业环境保护国家重点实验室）；Appraisal Center for Environmental Engineering, Ministry of Ecology and Environment（生态环境部环境工程评估中心）
VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration
Mohamed bin Zayed University of Artificial Intelligence（莫扎德·本·扎耶德人工智能大学）；Fraunhofer Institute for Open Communication Systems（弗劳恩霍夫开放通信系统研究所）；Technical University of Munich（慕尼黑技术大学）；Alibaba International Digital Commerce（阿里巴巴国际数字商务）
Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps
The University of Texas at Dallas（德克萨斯大学达拉斯分校）；Alabama Transportation Institute, The University of Alabama（阿拉巴马大学阿拉巴马交通研究所）；The University of Alabama（阿拉巴马大学）；Clemson University（克莱姆森大学）
Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study
Department of Data Science & AI, Monash University（数据科学与人工智能系，莫纳什大学）；Faculty of Law, Monash University（法学院，莫纳什大学）
Tower+: Bridging Generality and Translation Specialization in Multilingual LLMs
Unbabel；Instituto de Telecomunicações（电信研究所）；Instituto Superior Técnico & Universidade de Lisboa (Lisbon ELLIS Unit)（里斯本大学技术学院及里斯本大学（里斯本ELLIS单位））；MICS, CentraleSupélec, Université Paris-Saclay（MICS，CentraleSupélec，巴黎萨克雷大学）
Oxford Internet Institute, University of Oxford（牛津大学牛津互联网研究所）；The Alan Turing Institute（阿兰·图灵研究所）；Department of Electrical and Electronic Engineering, University of Surrey（萨里大学电气与电子工程系）
This article identifies a critical incompatibility between European notions of discrimination and existing statistical measures of fairness. First, we review the evidential requirements to bring a claim under EU non-discrimination law. Due to the disparate nature of algorithmic and human discrimination, the EU's current requirements are too contextual, reliant on intuition, and open to judicial interpretation to be automated. Second, we show how the legal protection offered by non-discrimination law is challenged when AI, not humans, discriminate. Humans discriminate due to negative attitudes …
Bridging the Communication Gap: Evaluating AI Labeling Practices for Trustworthy AI Development
Lamarr Institute for ML and AI（拉马尔机器学习与人工智能研究所）；TU Dortmund University（多特蒙德工业大学）；RC Trustworthy Data Science and Security（可信数据科学与安全研究中心）；Enterprise Computing（企业计算机构）；Smart City Science（智慧城市科学机构）
Bridging the Gap Between Scientific Laws Derived by AI Systems and Canonical Knowledge via Abductive Inference with AI-Noether
University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；IBM Research Madison, WI（IBM马歇尔实验室）；IBM Research Yorktown Heights, NY（IBM尤里卡高地实验室）；Imperial Business School（帝国商业学院）；Samsung AI Cambridge, UK（三星AI剑桥实验室）
The use of Structured English as a computation independent knowledge representation format for non-technical users in business rules representation has been proposed in OMGs Semantics and Business Vocabulary Representation (SBVR). In the legal domain we face a similar problem. Formal representation languages, such as OASIS LegalRuleML and legal ontologies (LKIF, legal OWL2 ontologies etc.) support the technical knowledge engineer and the automated reasoning. But, they can be hardly used directly by the legal domain experts who do not have a computer science background. In this paper we adapt t…
Peking University（北京大学）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）
Spiking Neural Networks (SNNs) have attracted great attention due to their distinctive characteristics of low power consumption and temporal information processing. ANN-SNN conversion, as the most commonly used training method for applying SNNs, can ensure that converted SNNs achieve comparable performance to ANNs on large-scale datasets. However, the performance degrades severely under low quantities of time-steps, which hampers the practical applications of SNNs to neuromorphic chips. In this paper, instead of evaluating different conversion errors and then eliminating these errors, we defin…
