Schizophrenia - Page 41 | AI Medical Compendium

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Showing 841-861 of 2,688 articles

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 thei...

Apr 2025 http://arxiv.org/abs/2504.21559v1

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 ...

Apr 2025 http://arxiv.org/abs/2504.19254v2

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 M...

Apr 2025 http://arxiv.org/abs/2504.17671v3

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 robus...

Apr 2025 http://arxiv.org/abs/2504.14395v1

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 advancement...

Apr 2025 http://arxiv.org/abs/2504.13123v2

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

Large language models (LLMs) frequently generate hallucinations-content that deviates from factual...

Apr 2025 http://arxiv.org/abs/2504.12691v1

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 Proc...

Apr 2025 http://arxiv.org/abs/2505.00008v1

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 hu...

Apr 2025 http://arxiv.org/abs/2504.10179v1

MedHal: An Evaluation Dataset for Medical Hallucination Detection

We present MedHal, a novel large-scale dataset specifically designed to evaluate if models can det...

Apr 2025 http://arxiv.org/abs/2504.08596v1

Hallucination, reliability, and the role of generative AI in science

Generative AI is increasingly used in scientific domains, from protein folding to climate modeling...

Apr 2025 http://arxiv.org/abs/2504.08526v1

Learning Fine-grained Domain Generalization via Hyperbolic State Space Hallucination

Fine-grained domain generalization (FGDG) aims to learn a fine-grained representation that can be ...

Apr 2025 http://arxiv.org/abs/2504.08020v1

Decoupling Contrastive Decoding: Robust Hallucination Mitigation in Multimodal Large Language Models

Although multimodal large language models (MLLMs) exhibit remarkable reasoning capabilities on com...

Apr 2025 http://arxiv.org/abs/2504.08809v1

HalluciNot: Hallucination Detection Through Context and Common Knowledge Verification

This paper introduces a comprehensive system for detecting hallucinations in large language model ...

(1)

Apr 2025 http://arxiv.org/abs/2504.07069v1

TARAC: Mitigating Hallucination in LVLMs via Temporal Attention Real-time Accumulative Connection

Large Vision-Language Models have demonstrated remarkable performance across various tasks; howeve...

Apr 2025 http://arxiv.org/abs/2504.04099v1

Vision-Amplified Semantic Entropy for Hallucination Detection in Medical Visual Question Answering

Multimodal large language models (MLLMs) have demonstrated significant potential in medical Visual...

Mar 2025 http://arxiv.org/abs/2503.20504v1

Bigger But Not Better: Small Neural Language Models Outperform Large Language Models in Detection of Thought Disorder

Disorganized thinking is a key diagnostic indicator of schizophrenia-spectrum disorders. Recently,...

(1)

Mar 2025 http://arxiv.org/abs/2503.20103v1

Exploring Hallucination of Large Multimodal Models in Video Understanding: Benchmark, Analysis and Mitigation

The hallucination of large multimodal models (LMMs), providing responses that appear correct but a...

Mar 2025 http://arxiv.org/abs/2503.19622v1

FactSelfCheck: Fact-Level Black-Box Hallucination Detection for LLMs

Large Language Models (LLMs) frequently generate hallucinated content, posing significant challeng...

(1)

Mar 2025 http://arxiv.org/abs/2503.17229v1

ECKGBench: Benchmarking Large Language Models in E-commerce Leveraging Knowledge Graph

Large language models (LLMs) have demonstrated their capabilities across various NLP tasks. Their ...

(2)

Mar 2025 http://arxiv.org/abs/2503.15990v1

Grounded Chain-of-Thought for Multimodal Large Language Models

Despite great progress, existing multimodal large language models (MLLMs) are prone to visual hall...

(1)

Mar 2025 http://arxiv.org/abs/2503.12799v2

Eye Movement Characteristics for Predicting a Transition to Psychosis: Longitudinal Changes and Implications.

BACKGROUND AND HYPOTHESIS: Substantive inquiry into the predictive power of eye movement (EM) featur...

Mar 2025 38245498