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Large Language Models (LLMs) have emerged as powerful tools for automating programming tasks, including security-related ones. However, they can also introduce vulnerabilities during code generation, fail to detect existing vulnerabilities,…

密码学与安全 · 计算机科学 2026-03-18 Enna Basic , Alberto Giaretta

Large Language Models (LLMs) have shown notable potential in code generation for optimization algorithms, unlocking exciting new opportunities. This paper examines how LLMs, rather than creating algorithms from scratch, can improve existing…

人工智能 · 计算机科学 2025-07-22 Camilo Chacón Sartori , Christian Blum

This Innovative Practice full paper explores how Large Language Models (LLMs) can enhance the teaching of code refactoring in software engineering courses through real-time, context-aware feedback. Refactoring improves code quality but is…

软件工程 · 计算机科学 2025-08-14 Anshul Khairnar , Aarya Rajoju , Edward F. Gehringer

Large Language Models (LLM) are already widely used to generate content for a variety of online platforms. As we are not able to safely distinguish LLM-generated content from human-produced content, LLM-generated content is used to train…

机器学习 · 计算机科学 2024-06-18 Martin Briesch , Dominik Sobania , Franz Rothlauf

Generative AI plays an increasing role during software engineering activities to make them, e.g., more efficient or provide better quality. However, it is often unclear how much benefit LLMs really provide. We concentrate on software…

软件工程 · 计算机科学 2026-01-28 Frank Elberzhager , Matthias Gerbershagen , Joshua Ginkel

Large language models (LLM) have revolutionized the processing of natural language. Although first benchmarks of the process modeling abilities of LLM are promising, it is currently under debate to what extent an LLM can generate good…

计算与语言 · 计算机科学 2025-03-19 Peter Fettke , Constantin Houy

Evaluation and ranking of large language models (LLMs) has become an important problem with the proliferation of these models and their impact. Evaluation methods either require human responses which are expensive to acquire or use pairs of…

计算与语言 · 计算机科学 2024-06-11 Amit Dhurandhar , Rahul Nair , Moninder Singh , Elizabeth Daly , Karthikeyan Natesan Ramamurthy

In this work, we provide a systematic analysis of how large language models (LLMs) contribute to solving planning problems. In particular, we examine how LLMs perform when they are used as problem solver, solution verifier, and heuristic…

人工智能 · 计算机科学 2024-12-16 Haoming Li , Zhaoliang Chen , Songyuan Liu , Yiming Lu , Fei Liu

The generative large language models (LLMs) are increasingly being used for data augmentation tasks, where text samples are LLM-paraphrased and then used for classifier fine-tuning. However, a research that would confirm a clear…

计算与语言 · 计算机科学 2024-08-30 Jan Cegin , Jakub Simko , Peter Brusilovsky

In this study, we investigated the effects of self-reflection in large language models (LLMs) on problem-solving performance. We instructed nine popular LLMs to answer a series of multiple-choice questions to provide a performance baseline.…

计算与语言 · 计算机科学 2025-03-17 Matthew Renze , Erhan Guven

Large-Language Models (LLMs) are changing the way learners acquire knowledge outside the classroom setting. Previous studies have shown that LLMs seem effective in generating to short and simple questions in introductory CS courses using…

编程语言 · 计算机科学 2026-03-09 Yihan Zhang , Brigitte Pientka , Xujie Si

Simulations, although powerful in accurately replicating real-world systems, often remain inaccessible to non-technical users due to their complexity. Conversely, large language models (LLMs) provide intuitive, language-based interactions…

计算与语言 · 计算机科学 2025-05-22 Jacob Kleiman , Kevin Frank , Joseph Voyles , Sindy Campagna

The development of highly fluent large language models (LLMs) has prompted increased interest in assessing their reasoning and problem-solving capabilities. We investigate whether several LLMs can solve a classic type of deductive reasoning…

计算与语言 · 计算机科学 2024-04-16 Spencer M. Seals , Valerie L. Shalin

Powerful generative Large Language Models (LLMs) are becoming popular tools amongst the general public as question-answering systems, and are being utilised by vulnerable groups such as children. With children increasingly interacting with…

密码学与安全 · 计算机科学 2024-03-18 Ellie Prosser , Matthew Edwards

Tabular machine learning problems often require time-consuming and labor-intensive feature engineering. Recent efforts have focused on using large language models (LLMs) to capitalize on their potential domain knowledge. At the same time,…

机器学习 · 计算机科学 2025-07-16 Jaris Küken , Lennart Purucker , Frank Hutter

The planning ability of Large Language Models (LLMs) has garnered increasing attention in recent years due to their remarkable capacity for multi-step reasoning and their ability to generalize across a wide range of domains. While some…

人工智能 · 计算机科学 2025-02-19 Mohamed Aghzal , Erion Plaku , Gregory J. Stein , Ziyu Yao

Information Visualization has been utilized to gain insights from complex data. In recent times, Large Language Models (LLMs) have performed very well in many tasks. In this paper, we showcase the capabilities of different popular LLMs to…

软件工程 · 计算机科学 2025-06-16 Saadiq Rauf Khan , Vinit Chandak , Sougata Mukherjea

Large Language Models have quickly become a central component of modern software development workflows, and software practitioners are increasingly integrating LLMs into various stages of the software development lifecycle. Despite the…

软件工程 · 计算机科学 2025-10-13 Italo Santos , Cleyton Magalhaes , Ronnie de Souza Santos

Large Language Models (LLMs) achieve impressive performance in a wide range of tasks, even if they are often trained with the only objective of chatting fluently with users. Among other skills, LLMs show emergent abilities in mathematical…

计算与语言 · 计算机科学 2024-06-12 Flavio Petruzzellis , Alberto Testolin , Alessandro Sperduti

Large language models (LLMs) have introduced new paradigms for recommender systems by enabling richer semantic understanding and incorporating implicit world knowledge. In this study, we propose a systematic taxonomy that classifies…