Psychiatry

Schizophrenia

Latest AI and machine learning research in schizophrenia for healthcare professionals.

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MIRAGE: Assessing Hallucination in Multimodal Reasoning Chains of MLLM

Multimodal hallucination in multimodal large language models (MLLMs) restricts the correctness of ...

Preemptive Hallucination Reduction: An Input-Level Approach for Multimodal Language Model

Visual hallucinations in Large Language Models (LLMs), where the model generates responses that ar...

Safeguarding Privacy of Retrieval Data against Membership Inference Attacks: Is This Query Too Close to Home?

Retrieval-augmented generation (RAG) mitigates the hallucination problem in large language models ...

Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning

Domain-specific instruction-tuning has become the defacto standard for improving the performance o...

Mitigating Hallucination in Large Vision-Language Models via Adaptive Attention Calibration

Large vision-language models (LVLMs) achieve impressive performance on multimodal tasks but often ...

BindEnergyCraft: Casting Protein Structure Predictors as Energy-Based Models for Binder Design

Protein binder design has been transformed by hallucination-based methods that optimize structure ...

Reinforced Informativeness Optimization for Long-Form Retrieval-Augmented Generation

Long-form question answering (LFQA) presents unique challenges for large language models, requirin...

PARTONOMY: Large Multimodal Models with Part-Level Visual Understanding

Real-world objects are composed of distinctive, object-specific parts. Identifying these parts is ...

Retrieval Visual Contrastive Decoding to Mitigate Object Hallucinations in Large Vision-Language Models

Despite significant advancements in Large Vision-Language Models, Object Hallucination (OH) remain...

Omni-R1: Reinforcement Learning for Omnimodal Reasoning via Two-System Collaboration

Long-horizon video-audio reasoning and fine-grained pixel understanding impose conflicting require...

Attention! You Vision Language Model Could Be Maliciously Manipulated

Large Vision-Language Models (VLMs) have achieved remarkable success in understanding complex real...

GUARDIAN: Safeguarding LLM Multi-Agent Collaborations with Temporal Graph Modeling

The emergence of large language models (LLMs) enables the development of intelligent agents capabl...

Image Tokens Matter: Mitigating Hallucination in Discrete Tokenizer-based Large Vision-Language Models via Latent Editing

Large Vision-Language Models (LVLMs) with discrete image tokenizers unify multimodal representatio...

MedScore: Factuality Evaluation of Free-Form Medical Answers

While Large Language Models (LLMs) can generate fluent and convincing responses, they are not nece...

More Thinking, Less Seeing? Assessing Amplified Hallucination in Multimodal Reasoning Models

Test-time compute has empowered multimodal large language models to generate extended reasoning ch...

Multi-Modal Spectral Parametrization Method (MMSPM) for analyzing EEG activity with distinct scaling regimes

Aperiodic neural activity has been the subject of intense research interest lately as it could ref...

Multi-Modal Spectral Parametrization Method (MMSPM) for analyzing EEG activity with distinct scaling regimes

Aperiodic neural activity has been the subject of intense research interest lately as it could ref...

Do You Keep an Eye on What I Ask? Mitigating Multimodal Hallucination via Attention-Guided Ensemble Decoding

Recent advancements in Large Vision-Language Models (LVLMs) have significantly expanded their util...

Seeing Far and Clearly: Mitigating Hallucinations in MLLMs with Attention Causal Decoding

Recent advancements in multimodal large language models (MLLMs) have significantly improved perfor...

Mitigating Hallucinations in Vision-Language Models through Image-Guided Head Suppression

Despite their remarkable progress in multimodal understanding tasks, large vision language models ...

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