large language models

AI-driven feature selection using only survey variable descriptions: large language models identify adolescent vaping predictors

Abstract Objective To evaluate the effectiveness of various Large Language Models (LLMs) in identifying reliable predictors of Electronic Nicotine Delivery Systems (ENDS) initiation among adolescents, using solely large-scale survey variable descriptions. Methods A cohort of 7,943 tobacco-naïve adolescents aged 12-16 y...

Step-level screening-driven reasoning enhancement for large language models in prefabricated substation fault diagnosis

Can a Small Model Learn to Look Before It Leaps? Dynamic Learning and Proactive Correction for Hallucination Detection

HumaniBench: A Human-Centric Framework for Large Multimodal Models Evaluation

Although recent large multimodal models (LMMs) show impressive progress on vision–language tasks, their alignment with human-centered (HC) principles such as fairness, ethics, inclusivity, empathy, and robustness is often overlooked. Existing LMM benchmarks are largely accuracy-agnostic. We present HumaniBench, a unifi...

Meticulously Unsupervised Entity Alignment With Large Language Models

A novel unsupervised entity alignment framework, named LLM‐Align, is presented to effectively leverage Large Language Models to precisely annotate entity pairs, which alleviates structural heterogeneity between aligned entities.

AI-supported extraction of functional tissue unit properties for Human Reference Atlas construction

Abstract The Human Reference Atlas (HRA) effort brings together experts from more than 25 international consortia to capture the multiscale organization of the human body—from large anatomical organ systems (macro) to the single-cell level (micro). Functional tissue units (FTU, meso) in 10 organs have been detailed and...

Perspective of software engineering researchers on machine learning practices regarding research, review, and education

Abstract Context Machine Learning (ML) significantly impacts Software Engineering (SE), but studies primarily focus on practitioners, neglecting researchers. This ignores practices and challenges in teaching, researching, or reviewing ML applications in SE. Objective This study aims to contribute to knowledge of the sy...

Personalization in professional academic search

In this paper, we investigated how academic search can profit from personalization by incorporating query history and background knowledge in the ranking of the results. We implemented both techniques in a language modelling framework, using the Indri search engine. For our experiments, we used the iSearch data collect...

Application, Progress, Challenges and Coping Strategies of Artificial Intelligence in Laser Diagnosis and Treatment of Dermatology

Selective photothermolysis is the basic theoretical foundation of laser therapy, and now a highly used diagnostic and treatment method in dermatology is based on it. Although laser therapy has shown some good curative effects on various skin diseases, clinical application still faces many problems: the parameters are n...

Application of Knowledge Graph-Based GraphRAG in Intelligent Question Answering Systems

Large language models (LLMs) are developing rapidly and have been widely applied in intelligent question answering, knowledge retrieval, education, healthcare, enterprise services, and other fields. However, LLMs still exhibit limitations in knowledge updating, understanding complex relationships, and tracing answer so...

ALGORITHMIC SMOOTHING AS NOETIC DEPRIVATION: THE PHENOMENON OF "ALGORITHMIC CASTRATION" AND THE BOUNDARIES OF AI IN HUMAN AUTHORS-OZARVEKS CREATIVITY

ABSTRACT This paper formulates and analyzes the concept of "Noetic Castration" (algorithmic smoothing) in the context of generative AI-assisted literary production. Operating strictly within syntactic and statistical optimization, Large Language Models (LLMs) interpret peak emotional, existential, and metaphysical expr...

A Survey on Reinforcement Learning Optimization Methods for Multi-Agent Collaboration of Large Language Models

The integration of reinforcement learning (RL) into the optimization of multi-agent collaboration for Large Language Models (LLMs) is an important combination of two advanced areas, Multi-Agent Systems (MAS) and LLMs. This paper thoroughly examines the main approaches, evaluation standards, recent progress and existing...