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Showing 820-840 of 2,684 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

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 du...

Apr 2025 http://arxiv.org/abs/2504.18027v1

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

Quantum computing education faces significant challenges due to its complexity and the limitations...

Apr 2025 http://arxiv.org/abs/2504.18603v1

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

RePOPE: Impact of Annotation Errors on the POPE Benchmark

Since data annotation is costly, benchmark datasets often incorporate labels from established imag...

Apr 2025 http://arxiv.org/abs/2504.15707v1

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

Preference alignment through Direct Preference Optimization (DPO) has demonstrated significant eff...

Apr 2025 http://arxiv.org/abs/2504.15619v1

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

Large language models (LLMs) have become a disruptive force in the industry, introducing unprecede...

Apr 2025 http://arxiv.org/abs/2504.14917v1

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

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 pa...

Apr 2025 http://arxiv.org/abs/2504.13957v1

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-grai...

Apr 2025 http://arxiv.org/abs/2504.12083v1

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

Psychiatry

Browse Categories

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

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

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

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

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

RePOPE: Impact of Annotation Errors on the POPE Benchmark

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

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

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

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

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

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

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

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

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

MedHal: An Evaluation Dataset for Medical Hallucination Detection

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

Learning Fine-grained Domain Generalization via Hyperbolic State Space Hallucination

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

Perception in Reflection

HalluciNot: Hallucination Detection Through Context and Common Knowledge Verification

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