large language models

A review of bias detection and fairness auditing techniques in LLMs

A thorough literature review is provided to encapsulate prior research on bias identification and fairness auditing, categorizing the findings according to various stages of study and proposing a unified pipeline for dataset integration and a modular framework for bias auditing.

Adapting NDBIs for Autistic Children Who Use Echolalia: A Cascading Coaching Approach to Supporting Communication and Language Development

This pilot study examined a cascading coaching model to train graduate student clinicians and caregivers to use adapted Naturalistic Developmental Behavioral Intervention (NDBI) techniques with autistic preschoolers who use echolalia. Adaptations were designed to incorporate children’s echolalic speech within intervent...

SciRep: A Ranking-Aware Representation Model for Scientific Text

Results demonstrate that the proposed ranking-aware distillation mechanism significantly enhances scientific text representation quality while maintaining efficient inference, offering a more effective contrastive learning method for domain-specific retrieval tasks.

Evaluation of a cornea-specialized large language model for diagnostic and management accuracy in complex corneal cases

A cornea-specialized large language model enhanced with RAG improved diagnostic accuracy in complex corneal cases, particularly among clinicians with lower baseline performance.

Retrieval Augmented Generation for Relative Distance-based Spatial Reasoning

Large Language Models (LLMs) can be applied to various real-world tasks, many of which require spatial reasoning, like pattern-based spatial search, Point of Interest (POI) recommendation, and itinerary or travel planning. However, on their own LLMs have been shown to lack reliable spatial reasoning capabilities, espec...

Generative large language models in medicine: a scoping review of recent methodological advances

By tracing the evolutionary trajectories of these methodologies, this scoping review provides a mechanism-centered framework to inform responsible model development and deployment in medical settings, tailored to task complexity, data characteristics, and resource constraints.

Comparative Analysis of Aesthetic Judgment Between Orthodontists, General Dentists, and Different Versions of Large Language Models Using the IOTN-AC Index

OpenAsset: A Pipeline for Open-World Asset Integration into Indoor Scene Synthesis

This paper proposes OpenAsset, a novel framework that converts single images of real-world objects into reusable, canonicalized 3D assets that seamlessly integrate into the scene synthesis workflow and introduces an automated canonicalization module that normalizes the scale, orientation, and coordinate systems of the...

A Knowledge-Enhanced Iterative Reasoning Framework for Accurate and Traceable Fault Diagnosis in Distributed Service Systems

Fault diagnosis in distributed systems is challenged by complex service dependencies, cascading anomaly propagation, and similar symptom patterns. This paper proposes a knowledge-enhanced iterative reasoning framework that integrates large language models (LLMs) with a numerical domain knowledge graph (KG). The KG enco...

A multimodal benchmark for editable constraint preserving history based CAD modeling

Editable CAD generation requires access to procedural history rather than final geometry alone, yet widely used CAD datasets typically omit explicit geometric constraints, native parametric assets, or rich semantic labels. We introduce EPICCAD, a multimodal benchmark for history-based CAD modeling that aligns compact c...

Large Language Models Versus Multidisciplinary Tumor Board Decisions in Thyroid Cancer

While LLMs demonstrate promising concordance in standardized thyroid cancer management, they are best positioned as supportive decision aids—such as in MDT preparation and workflow streamlining—rather than replacements for expert multidisciplinary evaluation, particularly in complex clinical scenarios.

A Project-based Learning Activity Involving Hybrid Intelligence in the Undergraduate Engineering Class

Artificial intelligence (AI) is increasingly embedded in academic practice, creating opportunities to support learning and improve educational outcomes. We implemented a project-based learning activity in an undergraduate engineering course in which students used meteorological data to construct a temperature time seri...