Psychiatry

Schizophrenia

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

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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 task...

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-answerin...

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

Vision language models (VLM) demonstrate sophisticated multimodal reasoning yet are prone to hallu...

High-Fidelity Novel View Synthesis via Splatting-Guided Diffusion

Despite recent advances in Novel View Synthesis (NVS), generating high-fidelity views from single ...

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

Large Vision-Language Models (LVLMs) have demonstrated impressive multimodal reasoning capabilitie...

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 ...

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, ...

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 surpassin...

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

Multimodal Large Language Models (MLLMs) represent the cutting edge of AI technology, with DeepSee...

(2)
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 automa...

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

Large Vision-Language Models (LVLMs) exhibit impressive multimodal reasoning capabilities but rema...

Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities

Mental illness is a widespread and debilitating condition with substantial societal and personal c...

Mirage in the Eyes: Hallucination Attack on Multi-modal Large Language Models with Only Attention Sink

Fusing visual understanding into language generation, Multi-modal Large Language Models (MLLMs) ar...

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Evaluating Hallucination in Large Vision-Language Models based on Context-Aware Object Similarities

Despite their impressive performance on multi-modal tasks, large vision-language models (LVLMs) te...

PAINT: Paying Attention to INformed Tokens to Mitigate Hallucination in Large Vision-Language Model

Large Vision Language Models (LVLMs) have demonstrated remarkable capabilities in understanding an...

Mitigating Hallucinations in Large Vision-Language Models via DPO: On-Policy Data Hold the Key

Hallucination remains a major challenge for Large Vision-Language Models (LVLMs). Direct Preferenc...

ChartInsighter: An Approach for Mitigating Hallucination in Time-series Chart Summary Generation with A Benchmark Dataset

Effective chart summary can significantly reduce the time and effort decision makers spend interpr...

Knowledge Graph-based Retrieval-Augmented Generation for Schema Matching

Traditional similarity-based schema matching methods are incapable of resolving semantic ambiguiti...

HALoGEN: Fantastic LLM Hallucinations and Where to Find Them

Despite their impressive ability to generate high-quality and fluent text, generative large langua...

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Feedback-Driven Vision-Language Alignment with Minimal Human Supervision

Vision-language models (VLMs) have demonstrated remarkable potential in integrating visual and lin...

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