large language models

LLMCloc: Compiler bug localization via large language model-based root cause analysis

VulnTACT: Predicting ATT&CK for ICS Tactics and Techniques from Vulnerability Information Using Domain-Adapted Large Language Models

A large language model and knowledge graph collaborative generative framework for aircraft manufacturing system design in MBSE

Performance of Large Language Models in Answering Juvenile Systemic Lupus Erythematosus Questions: A Blinded Expert-based Comparative Study

Background: Large language models are increasingly used to obtain medical information, but their performance in juvenile systemic lupus erythematosus remains insufficiently characterized. Objective: To compare expert-rated response quality and clinical relevance across 4 artificial intelligence (AI) models using standa...

Shallow Lexical, Deep Source: A Hierarchical Examination of LLM Bias in News Evaluation

Large language models (LLMs) increasingly mediate how people read the news, but how deeply their evaluations are shaped by textual cues remains unclear. This study manipulated news coverage of the Columbia Gaza campus protests at two levels: a shallow, lexical level (adjective framing: left, right, neutral, original) a...

What Users Say About Reimbursable Digital Therapeutics in Germany: Large-Scale App Store Review Analysis Using a Large Language Model

Abstract Background In 2019, Germany introduced a unique regulatory framework for digital therapeutics (DTx), termed digital health applications (DiGAs) in Germany, with the goal of integrating evidence-based DTx into statutory health care. DTx are eligible for reimbursement by statutory health insurance if manufacture...

Retrieval-Augmented Generation Applications and Development in the Field of Electronic Test Instruments

The rapid development of artificial intelligence (AI), especially the fast iteration and widespread deployment of large language models (LLMs), has ushered in a new paradigm for the intelligent augmentation of electronic test instruments and automatic test systems (ATS). By combining the powerful reasoning and generati...

MacroRisk-agent: a retrieval-augmented multi-agent framework with deterministic validation gates for auditable stress testing

Abstract Large language model (LLM)-based multi-agent systems face three open challenges in high-stakes decision support: hallucination control in structured output generation, coupling of semantic reasoning with deterministic numerical simulation, and end-to-end auditability of multi-step agentic workflows. We present...

Bias in Large Language Models Through the Lens of Data Feminism

This paper systematically examines bias in Large Language Models (LLMs) through the lens of Data Feminism. The research demonstrates that bias in LLMs is not an issue of technical neutrality, but rather a systematic reproduction of societal power structures embedded in data collection, algorithmic design, and output ge...

AI versus human coding of NIH grant abstracts

Large language models (LLMs) are increasingly used for qualitative analysis in substance use research, yet their performance relative to human coders remains underexplored. This study compares ChatGPT-4.0 with human coders in performing qualitative coding tasks using NIH grant abstracts as a test case, focusing on the...

递归自改进系统(RSI)的本质是自发对称破缺:智能爆炸有界—— AI研发切记盲目投入

传统智能爆炸假说(I.J.Good,1965)假定递归自改进(RSI)闭环一旦触发,正反馈持续放大,会走向无上限超指数增长(硬起飞 FOOM)。本文提出智能爆炸本质是递归自改进系统内部发生自发对称破缺(SSB)相变:爆炸仅发生在临界点附近极窄时间窗口;系统落入势能最低的主稳态之后,爆发自动收敛终止,不存在无限起飞。破缺选择不是随机沉降,优先落向能量更低的主...

LLM Hallucination of Citations in Economics Persists With Web‐Enabled Models

ABSTRACT Despite significant advances since 2023 in large language model (LLM) capabilities and the introduction of web‐enabled search functionality, citation hallucination remains a persistent problem in AI‐generated academic content. We test GPT‐4o, DeepSeek V3, web‐enabled GPT‐4o, and web‐enabled GPT‐5.1 across prom...