large language models

Large language models as human proxies

Privacy-Constrained Distributionally Robust Detection and Collaborative Attribution of LLM-Generated Text

FedRAT is proposed, a privacy-preserving federated framework that couples paired paraphrase consistency, generator-family auxiliary supervision, differentially private client updates, and loss-dependent aggregation within a unified detection setting.

LIDR-TB: large language model-integrated platform for traceable drug repurposing in tuberculosis

Tuberculosis (TB) remains a global challenge exacerbated by the ongoing emergence of drug-resistant strains. Although drug repurposing offers a cost-effective strategy to expedite therapeutic discovery, its application is impeded by the lack of unified, disease-specific frameworks that integrate fragmented chemical and...

Dual-BERT adversarial learning for Hausa user-generated text normalization

Text normalization (TN) is a crucial preprocessing task in Natural Language Processing (NLP) that enables the conversion of noisy, informal texts into standardized forms suitable for downstream applications, such as machine translation, speech recognition, text-to-speech systems, sentiment analysis, and information ret...

REIGN: Regime-Enhanced Intelligent Granger Network for Nonstationary Causal Discovery

The Regime-Enhanced Intelligent Granger Network is proposed, a novel causal discovery framework specifically engineered for nonstationary time series and achieves an Optimal F1 score of 0.529, substantially outperforming PCMCI+ and PC in structural edge recovery.

Systemic Risks and Governance of Advanced Artificial Intelligence: A Structured Literature Review

A synthesized six-risk taxonomy; a four-level governance model spanning the individual, developer, state, and international levels; and a critical appraisal of the evidentiary basis for each risk claim suggest that dangerous outcomes are structurally plausible, though not inevitable, and that coordinated multilevel gov...

Artificial intelligence in improving reading comprehension of K-12 students: A systematic literature review

The educational use of artificial intelligence (AI) has expanded rapidly in recent years, offering new possibilities for individualized instruction and learner support. Despite its growing relevance, AI’s effectiveness in the development of reading comprehension among K-12 students remains limited. This systematic revi...

Multicenter Evaluation of Large Language Models Versus Hepatologists for Prognostic Prediction in Drug‐Induced Liver Injury

ABSTRACT Background & Aims Drug‐induced liver injury (DILI) would progress to chronicity or death. Large language models (LLMs) may enhance clinical decision‐making, yet their utility relative to physicians in DILI remains unclear. Therefore, we evaluated their performance in predicting DILI outcomes. Methods We enroll...

Comparing Large Language Models for Aspect-Based Sentiment Analysis of Student Experience from Russian-Language Online Course Reviews

Uncovering Latent Scientific Structures in Albanian Texts Using Transformer-Based Clustering

The results suggest that the section-based representation produces clearer and more semantically coherent clusters by focusing on the main thematic information and reducing non-discriminative content, and may be adapted to other languages and low-resource research domains.

An intelligent OCR-LLM prescription review system for inpatient narcotic and psychotropic drugs

This study aimed to construct an intelligent prescription review system for inpatient narcotic and psychotropic drugs based on deep learning-based optical character recognition (OCR) and a local large language model (LLM) and evaluate its review performance and closed-loop management value. This study adopted a combine...

Corpus-level behavioral intensity in human–AI conversations

This study proposes a transparent rule-based framework for estimating persistence, delegation-related lexical patterns, and refinement-related lexical markers without inferring psychological dependence in conversational generative models.