Schizophrenia

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

Mental illness is a widespread and debilitating condition with substantial societal and personal costs. Traditional diagnostic and treatment approaches, such as self-reported questionnaires and psychotherapy sessions, often impose significant burdens on both patients and clinicians, limiting accessibility and efficiency. Recent advances in Artificial Intelligence (AI), particularly in Natural La...

CHiP: Cross-modal Hierarchical Direct Preference Optimization for Multimodal LLMs

Multimodal Large Language Models (MLLMs) still struggle with hallucinations despite their impressive capabilities. Recent studies have attempted to mitigate this by applying Direct Preference Optimization (DPO) to multimodal scenarios using preference pairs from text-based responses. However, our analysis of representation distributions reveals that multimodal DPO struggles to align image and te...

Scaling Large Vision-Language Models for Enhanced Multimodal Comprehension In Biomedical Image Analysis

Large language models (LLMs) have demonstrated immense capabilities in understanding textual data and are increasingly being adopted to help researc...

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) are revolutionizing visual-language applications. Ye...

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) tend to suffer from hallucinations. An important typ...

Measuring and Mitigating Hallucinations in Vision-Language Dataset Generation for Remote Sensing

Vision language models have achieved impressive results across various fields. However, adoption in remote sensing remains limited, largely due to t...

Comprehensive Modeling and Question Answering of Cancer Clinical Practice Guidelines using LLMs

The updated recommendations on diagnostic procedures and treatment pathways for a medical condition are documented as graphical flows in Clinical Pr...

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 and describing visual content, achieving state-of-th...

Question-to-Question Retrieval for Hallucination-Free Knowledge Access: An Approach for Wikipedia and Wikidata Question Answering

This paper introduces an approach to question answering over knowledge bases like Wikipedia and Wikidata by performing "question-to-question" matchi...

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 Preference Optimization (DPO) has gained increasing attenti...

Exploring the Inquiry-Diagnosis Relationship with Advanced Patient Simulators

Recently, large language models have shown great potential to transform online medical consultation. Despite this, most research targets improving d...

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 interpreting charts, enabling precise and efficient commu...

Knowledge Graph-based Retrieval-Augmented Generation for Schema Matching

Traditional similarity-based schema matching methods are incapable of resolving semantic ambiguities and conflicts in domain-specific complex mappin...

HALoGEN: Fantastic LLM Hallucinations and Where to Find Them

Despite their impressive ability to generate high-quality and fluent text, generative large language models (LLMs) also produce hallucinations: stat...

MedCT: A Clinical Terminology Graph for Generative AI Applications in Healthcare

We introduce the world's first clinical terminology for the Chinese healthcare community, namely MedCT, accompanied by a clinical foundation model M...

ECBench: Can Multi-modal Foundation Models Understand the Egocentric World? A Holistic Embodied Cognition Benchmark

The enhancement of generalization in robots by large vision-language models (LVLMs) is increasingly evident. Therefore, the embodied cognitive abili...

Seeing with Partial Certainty: Conformal Prediction for Robotic Scene Recognition in Built Environments

In assistive robotics serving people with disabilities (PWD), accurate place recognition in built environments is crucial to ensure that robots navi...

Feedback-Driven Vision-Language Alignment with Minimal Human Supervision

Vision-language models (VLMs) have demonstrated remarkable potential in integrating visual and linguistic information, but their performance is ofte...

Motif Discovery Framework for Psychiatric EEG Data Classification

In current medical practice, patients undergoing depression treatment must wait four to six weeks before a clinician can assess medication response ...

RAG-Check: Evaluating Multimodal Retrieval Augmented Generation Performance

Retrieval-augmented generation (RAG) improves large language models (LLMs) by using external knowledge to guide response generation, reducing halluc...