large language models

Large language models for digital mental health: an HCI-centered scoping review

Large language models (LLMs) are rapidly reshaping digital mental health, yet how these systems are designed, used, and evaluated remains poorly characterized. We conducted an HCI-centered scoping review of 84 studies examining mental health tasks, stakeholders, interaction paradigms, foundation models, evaluation meth...

An Explainable Rule-Based Resume-Job Description Skill Matching System with LLM-Assisted Feedback

Automated resume screening and Applicant Tracking Systems (ATS) increasingly incorporate machine learning components and neural language models. While deep learning methods can capture complex linguistic patterns, their computational overhead, potential opacity, and sensitivity to unvetted training distributions pose p...

Quality optimization with low positive factual hallucination for the HPI in hyperthyroidism admission notes using multi agent LLM with RAG

Admission note quality affects clinical safety and research reliability, yet resident drafts often suffer from missing information, unclear logic, and other issues, requiring time-consuming manual review. Current large language models (LLMs) focus on text generation or formatting correction but rarely optimize clinical...

VLM-Mapper: Multimodal vision-language models for prior guided lane-level HD map construction

The automated construction of High-Definition (HD) maps from remote sensing data is essential for modern intelligent transportation systems and spatial data infrastructure. While imagery provides a scalable solution for lane-level HD map construction, traditional discriminative models often struggle in complex scenario...

RECUR: Retrieval-Based customer review analysis framework utilizing nonparametric masked language modeling

Social simulations with large language model risk Utopian illusion

Reliable simulation of human behaviour is essential for explaining, predicting, and intervening in our society. Recent advances in large language models (LLMs) have shown promise in emulating human behaviours, interactions, and decision-making, offering a powerful new lens for social science studies. However, the exten...

Human-like content analysis for generative AI with language-grounded sparse encoders

The rapid development of generative AI has transformed content creation, communication, and human development. However, this technology raises profound concerns in high-stakes domains, demanding rigorous methods to analyze and evaluate AI-generated content. While existing analytic methods often treat images as indivisi...

CyberRoBERTa: A Lightweight Transformer-Based Language Model for IoT Intrusion Detection

With the accelerated proliferation of the Internet of Things (IoT) and its widespread use in sensitive areas such as healthcare, homes, and factories, security has become an increasingly critical concern. IoT remains one of the most challenging domains to secure, lacking the manageable scale, well-maintained software,...

Research on Model Construction of Intelligent Command and Control System Based on Large Language Model

Retrofitting language models to operate over bytes

Abstract Recent advances in artificial intelligence (AI) have largely been driven by large language models, deep neural networks that operate over discrete units called tokens. To represent text, most large language models use words or word fragments as the tokens, known as subword tokenization 1 . Subword tokenization...

Beyond the prompt for pedestrian traffic and crowd behavior management: PedAgent, a Large Language Model empowered Agent with an integrated simulating-analyzing-optimizing functional chain

Efficient VLM Inference System With LoRA Adapters at the Edge

Vision Language Models (VLMs) extend large language models with visual perception, enabling complex vision tasks at the edge. Low-rank adaptation (LoRA) adapters offer a lightweight method to inject domain-specific knowledge into a shared base VLM, making them attractive for edge serving where concurrent mobile clients...