large language models

Non-extensive statistics of the glioblastoma transcriptome: analysis code, derived results and validation-cohort inputs

Analysis code, derived result tables, figures and validation-cohort inputs for a study of non-extensive (Tsallis) statistics in the glioblastoma transcriptome. Expression deviations of each sample are fitted with q-Gaussian laws by maximum likelihood. In a longitudinal GLASS cohort (230 libraries from 115 IDH-wildtype...

A Survey on Table Mining with Large Language Models: Challenges, Advancements and Prospects

This survey conducts a comprehensive review of the literature on table mining with Large Language Models, exploring the challenges unique to this domain, such as heterogeneous table structures, contextual ambiguity, and domain-specific knowledge requirements.

Evaluating Large Language Models for Triple Extraction from Chinese Historical Geographic Sources: A Case Study Using Sources from the China Historical GIS Project

Multimodal medical diagnosis: a mini review of LLM–vision fusion models in low-resource healthcare settings

Recent advances in large language models (LLMs) and vision transformers have enabled multimodal systems that integrate clinical text with medical imaging for diagnostic decision-making. While these systems show promising results on benchmark datasets in well-resourced research settings, their applicability in low-resou...

What clinicians should know about how large language models reason

Phase-Based Dynamic Prediction of Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage: Comparison of a Large Language Model with Intensive Care Specialists

Background and Objectives: Delayed cerebral ischemia (DCI) is a major determinant of poor outcome after aneurysmal subarachnoid hemorrhage (aSAH), yet early risk prediction remains difficult, particularly in sedated or ventilated intensive care patients. We evaluated whether a large language model (LLM) could predict D...

Optimization of Machine Learning–Based Recommendation Systems on E-Commerce Platforms

The findings indicate that deep learning, hybrid recommendation models, sequential recommendation approaches, and large language model–based systems significantly enhance recommendation accuracy and personalization in e-commerce environments.

Idiomatic Creativity and Emergent Fixedness: Gradient Lexical Specification in Large-Scale Cross-Linguistic Corpora

Abstract This paper demonstrates that idiomatic fixedness is manipulation-dependent: the constructional elements that resist omission in idiom variants are systematically different from those that resist replacement. Using a manually verified dataset of over 14,000 idiomatic variants across 25 idioms in three languages...

ChatSeven: An Agentic AI-Based Multi-Agent Platform for Multi-Channel Customer Conversation Management and Campaign Automation

Abstract—Customer engagement platforms increasingly re-quire artificial intelligence (AI), multi-channel messaging, work-flow automation, and outbound campaign delivery within a single operational system. Traditional conversational agents—including rule-based chatbots, retrieval-augmented generation (RAG) only bots, an...

Exploring the Development of AI-Mediated Competence for Sustainable Translation and Interpreting Education: A Bibliometric Review

The keywords evidence points to a shift from machine translation, post-editing, and translation technology training toward AI literacy, evaluative judgement, output verification, feedback practices, ethical responsibility, professional agency, and human-AI collaboration.

Large language models-driven public perception modeling in metro-led underground space: a case study of Hong Kong

Reimagining biomedical science workflows in the age of large language models

Large language models (LLMs) are generative artificial intelligence (AI) models that are rapidly reshaping the practice of biomedical science. Their ability to synthesize literature, generate analytical code, and interface with multimodal data offers a new framework for accelerating discovery. Yet their integration int...