Schizophrenia

Uncertainty Modeling in Multimodal Speech Analysis Across the Psychosis Spectrum

Capturing subtle speech disruptions across the psychosis spectrum is challenging because of the inherent variability in speech patterns. This variability reflects individual differences and the fluctuating nature of symptoms in both clinical and non-clinical populations. Accounting for uncertainty in speech data is essential for predicting symptom severity and improving diagnostic precision. Spe...

Exploring Causes and Mitigation of Hallucinations in Large Vision Language Models

Large Vision-Language Models (LVLMs) integrate image encoders with Large Language Models (LLMs) to process multi-modal inputs and perform complex visual tasks. However, they often generate hallucinations by describing non-existent objects or attributes, compromising their reliability. This study analyzes hallucination patterns in image captioning, showing that not all tokens in the generation pr...

The Role of Background Information in Reducing Object Hallucination in Vision-Language Models: Insights from Cutoff API Prompting

Vision-Language Models (VLMs) occasionally generate outputs that contradict input images, constraining their reliability in real-world applications....

Hallucination Detection in Large Language Models with Metamorphic Relations

Large Language Models (LLMs) are prone to hallucinations, e.g., factually incorrect information, in their responses. These hallucinations present ch...

Reducing Hallucinations of Medical Multimodal Large Language Models with Visual Retrieval-Augmented Generation

Multimodal Large Language Models (MLLMs) have shown impressive performance in vision and text tasks. However, hallucination remains a major challeng...

MedHallu: A Comprehensive Benchmark for Detecting Medical Hallucinations in Large Language Models

Advancements in Large Language Models (LLMs) and their increasing use in medical question-answering necessitate rigorous evaluation of their reliabi...

SegSub: Evaluating Robustness to Knowledge Conflicts and Hallucinations in Vision-Language Models

Vision language models (VLM) demonstrate sophisticated multimodal reasoning yet are prone to hallucination when confronted with knowledge conflicts,...

High-Fidelity Novel View Synthesis via Splatting-Guided Diffusion

Despite recent advances in Novel View Synthesis (NVS), generating high-fidelity views from single or sparse observations remains a significant chall...

CutPaste&Find: Efficient Multimodal Hallucination Detector with Visual-aid Knowledge Base

Large Vision-Language Models (LVLMs) have demonstrated impressive multimodal reasoning capabilities, but they remain susceptible to hallucination, p...

Lost in Transcription, Found in Distribution Shift: Demystifying Hallucination in Speech Foundation Models

Speech foundation models trained at a massive scale, both in terms of model and data size, result in robust systems capable of performing multiple s...

LanP: Rethinking the Impact of Language Priors in Large Vision-Language Models

Large Vision-Language Models (LVLMs) have shown impressive performance in various tasks. However, LVLMs suffer from hallucination, which hinders the...

A Survey of LLM-based Agents in Medicine: How far are we from Baymax?

Large Language Models (LLMs) are transforming healthcare through the development of LLM-based agents that can understand, reason about, and assist w...

HuDEx: Integrating Hallucination Detection and Explainability for Enhancing the Reliability of LLM responses

Recent advances in large language models (LLMs) have shown promising improvements, often surpassing existing methods across a wide range of downstre...

DeepSeek on a Trip: Inducing Targeted Visual Hallucinations via Representation Vulnerabilities

Multimodal Large Language Models (MLLMs) represent the cutting edge of AI technology, with DeepSeek models emerging as a leading open-source alterna...

Enhancing Knowledge Graph Construction: Evaluating with Emphasis on Hallucination, Omission, and Graph Similarity Metrics

Recent advancements in large language models have demonstrated significant potential in the automated construction of knowledge graphs from unstruct...

Multimodal Data-Driven Classification of Mental Disorders: A Comprehensive Approach to Diagnosing Depression, Anxiety, and Schizophrenia

This study investigates the potential of multimodal data integration, which combines electroencephalogram (EEG) data with sociodemographic character...

Mitigating Object Hallucinations in Large Vision-Language Models via Attention Calibration

Large Vision-Language Models (LVLMs) exhibit impressive multimodal reasoning capabilities but remain highly susceptible to object hallucination, whe...

MJ-VIDEO: Fine-Grained Benchmarking and Rewarding Video Preferences in Video Generation

Recent advancements in video generation have significantly improved the ability to synthesize videos from text instructions. However, existing model...

Assessing the use of Diffusion models for motion artifact correction in brain MRI

Magnetic Resonance Imaging generally requires long exposure times, while being sensitive to patient motion, resulting in artifacts in the acquired i...

MINT: Mitigating Hallucinations in Large Vision-Language Models via Token Reduction

Hallucination has been a long-standing and inevitable problem that hinders the application of Large Vision-Language Models (LVLMs) in domains that r...