large language models

A2H_Preclinical_Translation_Data

🧾 Description This dataset accompanies the study “Large-Scale Assessment of Animal-to-Human Drug Translation Using Natural Language Processing” (bioRxiv, DOI: 10.64898/2026.05.20.726540), which presents a computational framework for the large-scale assessment of animal-to-human translation in drug development, with a...

Probing Instruction Execution Stability of Large Language Models under Semantic Paraphrase

Large Language Models (LLMs) are commonly controlled through natural-language instructions, yet equivalentprompts can yield inconsistent behavior. While prior work has largely evaluated prompt effectiveness throughtask accuracy, less attention has been paid to the stability of instruction execution under paraphrase. In...

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...