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We introduce a comprehensive Linguistic Benchmark designed to evaluate the limitations of Large Language Models (LLMs) in domains such as logical reasoning, spatial intelligence, and linguistic understanding, among others. Through a series…

人工智能 · 计算机科学 2024-06-04 Sean Williams , James Huckle

The rapid development of Large Language Models (LLMs) demonstrates remarkable multilingual capabilities in natural language processing, attracting global attention in both academia and industry. To mitigate potential discrimination and…

In recent years, large pre-trained language models (LLMs) have demonstrated the ability to follow instructions and perform novel tasks from a few examples. The possibility to parameterise an LLM through such in-context examples widens their…

Large language models (LLMs) have been applied to tasks in healthcare, ranging from medical exam questions to responding to patient questions. With increasing institutional partnerships between companies producing LLMs and healthcare…

计算与语言 · 计算机科学 2024-04-10 Jesutofunmi A. Omiye , Haiwen Gui , Shawheen J. Rezaei , James Zou , Roxana Daneshjou

The rapid advancement of Large Language Models (LLMs) has opened new avenues in education. This study examines the use of LLMs in supporting learning in machine learning education; in particular, it focuses on the ability of LLMs to…

计算机与社会 · 计算机科学 2025-05-27 Smitha Kumar , Michael A. Lones , Manuel Maarek , Hind Zantout

Large language models (LLMs) closely interact with humans, and thus need an intimate understanding of the cultural values of human society. In this paper, we explore how open-source LLMs make judgments on diverse categories of cultural…

计算与语言 · 计算机科学 2024-12-13 Minsang Kim , Seungjun Baek

Large Language Models (LLMs) are acquiring a wider range of capabilities, including understanding and responding in multiple languages. While they undergo safety training to prevent them from answering illegal questions, imbalances in…

计算与语言 · 计算机科学 2025-03-18 Likai Tang , Niruth Bogahawatta , Yasod Ginige , Jiarui Xu , Shixuan Sun , Surangika Ranathunga , Suranga Seneviratne

Recovering the structure of causal graphical models from observational data is an essential yet challenging task for causal discovery in scientific scenarios. Domain-specific causal discovery usually relies on expert validation or prior…

人工智能 · 计算机科学 2025-08-27 Taiyu Ban , Lyuzhou Chen , Derui Lyu , Xiangyu Wang , Qinrui Zhu , Qiang Tu , Huanhuan Chen

The rapid advancement of Large Language Models (LLMs) has opened up new opportunities for leveraging artificial intelligence in a variety of application domains, including cybersecurity. As the volume and sophistication of cyber threats…

密码学与安全 · 计算机科学 2025-09-23 Hanxiang Xu , Shenao Wang , Ningke Li , Kailong Wang , Yanjie Zhao , Kai Chen , Ting Yu , Yang Liu , Haoyu Wang

Large language models (LLMs) offer emerging opportunities for psychological and behavioral research, but methodological guidance is lacking. This article provides a framework for using LLMs as psychological simulators across two primary…

计算机与社会 · 计算机科学 2026-04-07 Zhicheng Lin

Large language models (LLMs) have achieved remarkable success across natural language processing tasks, yet their widespread deployment raises pressing concerns around privacy, copyright, security, and bias. Machine unlearning has emerged…

计算与语言 · 计算机科学 2026-01-21 Tyler Lizzo , Larry Heck

Large language models (LLMs) are demonstrating increasing prowess in cybersecurity applications, creating creating inherent risks alongside their potential for strengthening defenses. In this position paper, we argue that current efforts to…

密码学与安全 · 计算机科学 2025-02-04 Kamilė Lukošiūtė , Adam Swanda

Large Language Models (LLMs) have exploded a new heatwave of AI for their ability to engage end-users in human-level conversations with detailed and articulate answers across many knowledge domains. In response to their fast adoption in…

The open-endedness of large language models (LLMs) combined with their impressive capabilities may lead to new safety issues when being exploited for malicious use. While recent studies primarily focus on probing toxic outputs that can be…

计算与语言 · 计算机科学 2023-11-30 Jiaxin Wen , Pei Ke , Hao Sun , Zhexin Zhang , Chengfei Li , Jinfeng Bai , Minlie Huang

Large Language Models (LLMs) have become a milestone in the field of artificial intelligence and natural language processing. However, their large-scale deployment remains constrained by the need for significant computational resources.…

计算与语言 · 计算机科学 2025-08-07 Julián Camilo Velandia Gutiérrez

Despite the fact that large language models (LLMs) show exceptional skill in instruction following tasks, this strength can turn into a vulnerability when the models are required to disregard certain instructions. Instruction-following…

计算与语言 · 计算机科学 2025-08-12 Yerin Hwang , Yongil Kim , Jahyun Koo , Taegwan Kang , Hyunkyung Bae , Kyomin Jung

The advent of Large Language Models (LLMs) has revolutionized various applications by providing advanced natural language processing capabilities. However, this innovation introduces new cybersecurity challenges. This paper explores the…

密码学与安全 · 计算机科学 2024-06-18 Stephen Burabari Tete

Large Language Models (LLMs) possess the potential to exert substantial influence on public perceptions and interactions with information. This raises concerns about the societal impact that could arise if the ideologies within these models…

计算与语言 · 计算机科学 2024-06-19 Kai Chen , Zihao He , Jun Yan , Taiwei Shi , Kristina Lerman

The causal capabilities of large language models (LLMs) are a matter of significant debate, with critical implications for the use of LLMs in societally impactful domains such as medicine, science, law, and policy. We conduct a "behavorial"…

人工智能 · 计算机科学 2024-08-21 Emre Kıcıman , Robert Ness , Amit Sharma , Chenhao Tan

Large language models (LLMs) are revolutionizing every aspect of society. They are increasingly used in problem-solving tasks to substitute human assessment and reasoning. LLMs are trained on what humans write and are thus exposed to human…

软件工程 · 计算机科学 2025-10-14 Fengfei Sun , Ningke Li , Kailong Wang , Lorenz Goette