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Large language models (LLMs) are known to perform well on language tasks, but struggle with reasoning tasks. This paper explores the ability of LLMs to play the 2D puzzle game Baba is You, in which players manipulate rules by rearranging…

人工智能 · 计算机科学 2025-06-25 Fien van Wetten , Aske Plaat , Max van Duijn

As large language models (LLMs) continue to advance, evaluating their comprehensive capabilities becomes significant for their application in various fields. This research study comprehensively evaluates the language, vision, speech, and…

Previous learning-based vulnerability detection methods relied on either medium-sized pre-trained models or smaller neural networks from scratch. Recent advancements in Large Pre-Trained Language Models (LLMs) have showcased remarkable…

软件工程 · 计算机科学 2024-01-30 Xin Zhou , Ting Zhang , David Lo

Large language models (LLMs) such as ChatGPT and GPT-4 have shown impressive performance in complex reasoning tasks. However, it is difficult to know whether the models are reasoning based on deep understandings of truth and logic, or…

计算与语言 · 计算机科学 2023-10-11 Boshi Wang , Xiang Yue , Huan Sun

Large language models (LLMs) have recently demonstrated great success in generating and understanding natural language. While they have also shown potential beyond the domain of natural language, it remains an open question as to what…

计算与语言 · 计算机科学 2024-10-11 Muhammad Umair Nasir , Steven James , Julian Togelius

This paper presents reports on a series of experiments with a novel dataset evaluating how well Large Language Models (LLMs) can mark (i.e. grade) open text responses to short answer questions, Specifically, we explore how well different…

计算与语言 · 计算机科学 2024-05-07 Owen Henkel , Adam Boxer , Libby Hills , Bill Roberts

Large Language Model (LLM) agents have been increasingly adopted as simulation tools to model humans in social science and role-playing applications. However, one fundamental question remains: can LLM agents really simulate human behavior?…

Large Language Models (LLMs) have shown remarkable progress across domains, yet their ability to perform inductive reasoning - inferring latent rules from sparse examples - remains limited. It is often assumed that chain-of-thought (CoT)…

计算机视觉与模式识别 · 计算机科学 2025-06-02 Haibo Jin , Peiyan Zhang , Man Luo , Haohan Wang

Empirical methods to examine the capability of Large Language Models (LLMs) to use Automated Theorem Prover (ATP) reasoning strategies are studied. We evaluate the performance of State of the Art models from December 2023 and August 2024 on…

人工智能 · 计算机科学 2025-09-18 Lachlan McGinness , Peter Baumgartner

Large language models (LLMs), such as GPT-3 and GPT-4, have demonstrated exceptional performance in various natural language processing tasks and have shown the ability to solve certain reasoning problems. However, their reasoning…

人工智能 · 计算机科学 2023-07-18 Adam Ishay , Zhun Yang , Joohyung Lee

This study evaluates the ability of GPT-4o to autonomously solve beginner-level offensive security tasks by connecting the model to OverTheWire's Bandit capture-the-flag game. Of the 25 levels that were technically compatible with a…

密码学与安全 · 计算机科学 2026-01-27 Isabelle Bakker , John Hastings

Modern large language models (LLMs) exhibit a remarkable capacity for role-playing, enabling them to embody not only human characters but also non-human entities. This versatility allows them to simulate complex human-like interactions and…

计算与语言 · 计算机科学 2024-03-15 Aobo Kong , Shiwan Zhao , Hao Chen , Qicheng Li , Yong Qin , Ruiqi Sun , Xin Zhou , Enzhi Wang , Xiaohang Dong

Large language models (LLMs) are currently at the forefront of intertwining AI systems with human communication and everyday life. Therefore, it is of great importance to evaluate their emerging abilities. In this study, we show that LLMs,…

计算与语言 · 计算机科学 2023-10-10 Thilo Hagendorff , Sarah Fabi

Recent advancements in large language models (LLMs) have expanded their capabilities beyond traditional text-based tasks to multimodal domains, integrating visual, auditory, and textual data. While multimodal LLMs have been extensively…

人工智能 · 计算机科学 2024-12-03 Nicholas R. Waytowich , Devin White , MD Sunbeam , Vinicius G. Goecks

Vision Language Models (VLMs) have demonstrated strong reasoning capabilities in Visual Question Answering (VQA) tasks; however, their ability to perform Theory of Mind (ToM) tasks, such as inferring human intentions, beliefs, and mental…

计算机视觉与模式识别 · 计算机科学 2025-04-25 Ximing Wen , Mallika Mainali , Anik Sen

Game development is a highly specialized task that relies on a complex game engine powered by complex programming languages, preventing many gaming enthusiasts from handling it. This paper introduces the Chat Game Engine (ChatGE) powered by…

人工智能 · 计算机科学 2024-12-17 Jiale Hong , Hongqiu Wu , Hai Zhao

Generative Pre-trained Transformer 4 (GPT-4) is the fourth-generation language model in the GPT series, developed by OpenAI, which promises significant advancements in the field of natural language processing (NLP). In this research…

计算与语言 · 计算机科学 2023-05-08 Jawid Ahmad Baktash , Mursal Dawodi

The development and popularization of large language models (LLMs) have raised concerns that they will be used to create tailor-made, convincing arguments to push false or misleading narratives online. Early work has found that language…

计算机与社会 · 计算机科学 2025-05-21 Francesco Salvi , Manoel Horta Ribeiro , Riccardo Gallotti , Robert West

The latest advancements in AI and deep learning have led to a breakthrough in large language model (LLM)-based agents such as GPT-4. However, many commercial conversational agent development tools are pipeline-based and have limitations in…

计算与语言 · 计算机科学 2023-09-08 Mina Foosherian , Hendrik Purwins , Purna Rathnayake , Touhidul Alam , Rui Teimao , Klaus-Dieter Thoben

We present a simple yet effective approach that can transform the OpenAI GPT-3.5 model into a reliable motion planner for autonomous vehicles. Motion planning is a core challenge in autonomous driving, aiming to plan a driving trajectory…

计算机视觉与模式识别 · 计算机科学 2023-12-06 Jiageng Mao , Yuxi Qian , Junjie Ye , Hang Zhao , Yue Wang