Schizophrenia

Predicting Diagnostic Progression to Schizophrenia or Bipolar Disorder via Machine Learning.

IMPORTANCE: The diagnosis of schizophrenia and bipolar disorder is often delayed several years despite illness typically emerging in late adolescence or early adulthood, which impedes initiation of targeted treatment.

Localizing Before Answering: A Hallucination Evaluation Benchmark for Grounded Medical Multimodal LLMs

Medical Large Multi-modal Models (LMMs) have demonstrated remarkable capabilities in medical data interpretation. However, these models frequently generate hallucinations contradicting source evidence, particularly due to inadequate localization reasoning. This work reveals a critical limitation in current medical LMMs: instead of analyzing relevant pathological regions, they often rely on lingu...

Mapping minds not averages: a scalable subject-specific manifold learning framework for neuroimaging data

Mental and cognitive representations are believed to reside on low-dimensional, non-linear manifolds embedded within high-dimensional brain activity...

Efficient and robust 3D blind harmonization for large domain gaps

Blind harmonization has emerged as a promising technique for MR image harmonization to achieve scale-invariant representations, requiring only targe...

MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness

With the widespread application of large language models (LLMs), the issue of generating non-existing facts, known as hallucination, has garnered in...

Black-Box Visual Prompt Engineering for Mitigating Object Hallucination in Large Vision Language Models

Large Vision Language Models (LVLMs) often suffer from object hallucination, which undermines their reliability. Surprisingly, we find that simple o...

Uncertainty Quantification for Language Models: A Suite of Black-Box, White-Box, LLM Judge, and Ensemble Scorers

Hallucinations are a persistent problem with Large Language Models (LLMs). As these models become increasingly used in high-stakes domains, such as ...

A Large Vision-Language Model based Environment Perception System for Visually Impaired People

It is a challenging task for visually impaired people to perceive their surrounding environment due to the complexity of the natural scenes. Their p...

Toward Personalizing Quantum Computing Education: An Evolutionary LLM-Powered Approach

Quantum computing education faces significant challenges due to its complexity and the limitations of current tools; this paper introduces a novel I...

Data-Driven Calibration of Prediction Sets in Large Vision-Language Models Based on Inductive Conformal Prediction

This study addresses the critical challenge of hallucination mitigation in Large Vision-Language Models (LVLMs) for Visual Question Answering (VQA) ...

RePOPE: Impact of Annotation Errors on the POPE Benchmark

Since data annotation is costly, benchmark datasets often incorporate labels from established image datasets. In this work, we assess the impact of ...

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization

Preference alignment through Direct Preference Optimization (DPO) has demonstrated significant effectiveness in aligning multimodal large language m...

POLYRAG: Integrating Polyviews into Retrieval-Augmented Generation for Medical Applications

Large language models (LLMs) have become a disruptive force in the industry, introducing unprecedented capabilities in natural language processing, ...

Hydra: An Agentic Reasoning Approach for Enhancing Adversarial Robustness and Mitigating Hallucinations in Vision-Language Models

To develop trustworthy Vision-Language Models (VLMs), it is essential to address adversarial robustness and hallucination mitigation, both of which ...

Low-hallucination Synthetic Captions for Large-Scale Vision-Language Model Pre-training

In recent years, the field of vision-language model pre-training has experienced rapid advancements, driven primarily by the continuous enhancement ...

Why and How LLMs Hallucinate: Connecting the Dots with Subsequence Associations

Large language models (LLMs) frequently generate hallucinations-content that deviates from factual accuracy or provided context-posing challenges fo...

A Scoping Review of Natural Language Processing in Addressing Medically Inaccurate Information: Errors, Misinformation, and Hallucination

Objective: This review aims to explore the potential and challenges of using Natural Language Processing (NLP) to detect, correct, and mitigate medi...

Naming is framing: How cybersecurity's language problems are repeating in AI governance

Language is not neutral; it frames understanding, structures power, and shapes governance. This paper argues that misnomers like cybersecurity and a...

Self-alignment of Large Video Language Models with Refined Regularized Preference Optimization

Despite recent advances in Large Video Language Models (LVLMs), they still struggle with fine-grained temporal understanding, hallucinate, and often...

The Future of MLLM Prompting is Adaptive: A Comprehensive Experimental Evaluation of Prompt Engineering Methods for Robust Multimodal Performance

Multimodal Large Language Models (MLLMs) are set to transform how machines process and generate human-like responses by integrating diverse modaliti...