Large language models (LLMs), consisting of billions and trillions of parameters, have demonstrated exceptional ability in natural language understanding (NLU) and natural language generation (NLG) tasks. Increases in their numbers of parameters and...
Vision–language models (VLMs) show strong potential for remote-sensing scene classification but still struggle with fine-grained categories and distribution shifts. We introduce a hierarchical prompting framework that decomposes recognition int...
Retrieval-Augmented Generation (RAG) combined with Large Language Models (LLMs) introduces a new paradigm for clinical-trial data analysis that is both real-time and knowledge-traceable. This study targets a multi-site, real-world data environment. I...
Auditability and regulatory compliance are increasingly required for deploying large language models (LLMs). Prior work typically targets isolated stages such as training or unlearning and lacks a unified mechanism for verifiable accountability acros...
The rapid growth of electric vehicle requires more rigid safety and reliability requirements for EV motors. But traditional methods struggle to deal with complex conditions, small samples, and cross-dataset generalization. Thus, we propose a framewor...
The accurate modelling of video memorability, or the intrinsic properties that render a piece of audiovisual content more likely to be remembered, will facilitate the development of automatic systems that are more efficient in retrieving, classifying...
Intelligent mining risk assessment (MIRA) is a vital approach for enhancing safety and operational efficiency in mining. In this study, we introduce MIRA-ChatGLM, which leverages pre-trained large language models (LLMs) for the domain of gas risk ass...