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arXiv:2603.19268v1 Announce Type: cross Abstract: Large language models (LLMs) in the direction of task adaptation and capability enhancement for professional fields demonstrate significant application potential. …
arXiv:2603.19271v1 Announce Type: cross Abstract: While many researchers use Large Language Models (LLMs) through chat-based access, their real potential lies in leveraging LLMs via application …
arXiv:2603.19272v1 Announce Type: cross Abstract: Differentiable Neural Computers (DNCs) were introduced as recurrent architectures equipped with an addressable external memory supporting differentiable read and write …
arXiv:2603.19273v1 Announce Type: cross Abstract: Safety alignment in large language models relies predominantly on English-language training data. When harmful intent is expressed in low-resource languages, …
arXiv:2603.19274v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) demonstrate considerable potential in clinical diagnostics, a domain that inherently requires synthesizing complex visual and …
arXiv:2603.19275v1 Announce Type: cross Abstract: Automatic summarization of radiology reports is an essential application to reduce the burden on physicians. Previous studies have widely used …
arXiv:2603.19276v1 Announce Type: cross Abstract: Automated short answer grading (ASAG) is critical for scaling educational assessment, yet large language models (LLMs) often struggle with hallucinations …
arXiv:2603.19278v1 Announce Type: cross Abstract: Modern Transformer-based models frequently suffer from miscalibration, producing overconfident predictions that do not reflect true empirical frequencies. This work investigates …
arXiv:2603.19280v1 Announce Type: cross Abstract: The rapid advancements in large language models and generative artificial intelligence (AI) capabilities are making their broad application in the …
arXiv:2603.19281v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has emerged as a widely adopted approach for enhancing LLMs in scenarios that demand extensive factual knowledge. …
arXiv:2603.19282v1 Announce Type: cross Abstract: In many real-world applications, large language models (LLMs) operate as independent agents without interaction, thereby limiting coordination. In this setting, …
arXiv:2603.19284v1 Announce Type: cross Abstract: With the rapid advancement of large language models (LLMs), LLM-based heuristic search methods have demonstrated strong capabilities in automated algorithm …
