large language models

Transl-AI-tion 2.0

Artificial intelligence (AI) is transforming translation and interpreting with unprecedented speed, raising fundamental questions about the future of cross-lingual communication. This edited volume brings together scholars and practitioners from around the world to examine the technological, ethical, legal and pedagog...

Proficiency informed knowledge tracing: Leveraging large language models to enhance predictive methods in mathematics learning

Predictive methods play a key role in learning systems to enable effective interventions. However, most existing approaches emphasize concept-level suggestions and pay limited attention to cognitive constructs aligned with instructional goals. For instance, in mathematics, mastery depends on the integrated and balanced...

Comparing large language models and prompting techniques to control an embodied persuasive coach

Embodied agents play a crucial role in applications designed to influence user behaviour. Traditionally, crafting these agents requires considerable human effort. Large Language Models (LLMs) might open the possibility of creating agents capable of autonomous, open-ended interactions without labour-intensive developmen...

A Multi-stage Hybrid Retrieval Framework for Knowledge-Augmented Question Answering

AI Agents for CRISPR Diagnostics: From Automation to Bounded Autonomy

Recent years have witnessed rapid advances in clustered regularly interspaced short palindromic repeats (CRISPR) diagnostics, artificial intelligence (AI), and self-driving laboratories for automation. CRISPR diagnostics have emerged as a promising strategy for earlier disease detection, with Cas12a and Cas13a targetin...

DRoG: Distance-Aware Retrieval and Dual-Role Reasoning on Graphs for Large Language Models

ReVerTree: Recursive Verification Tree for Self-checking Mathematical Reasoning

Assessing Graph Data Serialization and Context Selection for Knowledge-Augmented Question Answering with Large Language Models

Large language models (LLMs) can produce inaccurate answers even when relevant facts are supplied in the prompt. This study examines how graph data serialization, entity identifiers, and context selection affect factual question answering in knowledge-augmented prompting. Four synthetic knowledge graph datasets contain...

Experiments with Template-Based SPARQL Generation from Natural Language Questions

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...