Schizophrenia

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 utility in tasks like image captioning and visual question answering. However, they still struggle with object hallucination, where models generate descriptions that inaccurately reflect the visual content by including nonexistent objects or misrepresenting existing ones. While previous methods, such as...

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

Recent advancements in multimodal large language models (MLLMs) have significantly improved performance in visual question answering. However, they often suffer from hallucinations. In this work, hallucinations are categorized into two main types: initial hallucinations and snowball hallucinations. We argue that adequate contextual information can be extracted directly from the token interaction...

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

Despite their remarkable progress in multimodal understanding tasks, large vision language models (LVLMs) often suffer from "hallucinations", genera...

Steering LVLMs via Sparse Autoencoder for Hallucination Mitigation

Large vision-language models (LVLMs) have achieved remarkable performance on multimodal tasks such as visual question answering (VQA) and image capt...

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation

Pretrained generative models have opened new frontiers in brain decoding by enabling the synthesis of realistic texts and images from non-invasive b...

Multimodal Biomarkers for Schizophrenia: Towards Individual Symptom Severity Estimation

Studies on schizophrenia assessments using deep learning typically treat it as a classification task to detect the presence or absence of the disord...

OViP: Online Vision-Language Preference Learning

Large vision-language models (LVLMs) remain vulnerable to hallucination, often generating content misaligned with visual inputs. While recent approa...

Unified Cross-Modal Attention-Mixer Based Structural-Functional Connectomics Fusion for Neuropsychiatric Disorder Diagnosis

Gaining insights into the structural and functional mechanisms of the brain has been a longstanding focus in neuroscience research, particularly in ...

Physics-Guided Multi-View Graph Neural Network for Schizophrenia Classification via Structural-Functional Coupling

Clinical studies reveal disruptions in brain structural connectivity (SC) and functional connectivity (FC) in neuropsychiatric disorders such as sch...

Toward Reliable Biomedical Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models

Large language models (LLMs) have shown significant potential in scientific disciplines such as biomedicine, particularly in hypothesis generation, ...

DeepEyes: Incentivizing "Thinking with Images" via Reinforcement Learning

Large Vision-Language Models (VLMs) have shown strong capabilities in multimodal understanding and reasoning, yet they are primarily constrained by ...

Aligning Attention Distribution to Information Flow for Hallucination Mitigation in Large Vision-Language Models

Due to the unidirectional masking mechanism, Decoder-Only models propagate information from left to right. LVLMs (Large Vision-Language Models) foll...

Towards Omnidirectional Reasoning with 360-R1: A Dataset, Benchmark, and GRPO-based Method

Omnidirectional images (ODIs), with their 360{\deg} field of view, provide unparalleled spatial awareness for immersive applications like augmented ...

Multimodal RAG-driven Anomaly Detection and Classification in Laser Powder Bed Fusion using Large Language Models

Additive manufacturing enables the fabrication of complex designs while minimizing waste, but faces challenges related to defects and process anomal...

Selective Code Generation for Functional Guarantees

Large language models (LLMs) show human-level performance and their specialized descendants, code generation models, play core roles in solving comp...

Tianyi: A Traditional Chinese Medicine all-rounder language model and its Real-World Clinical Practice

Natural medicines, particularly Traditional Chinese Medicine (TCM), are gaining global recognition for their therapeutic potential in addressing hum...

Mitigating Hallucinations via Inter-Layer Consistency Aggregation in Large Vision-Language Models

Despite the impressive capabilities of Large Vision-Language Models (LVLMs), they remain susceptible to hallucinations-generating content that is in...

Mixture of Decoding: An Attention-Inspired Adaptive Decoding Strategy to Mitigate Hallucinations in Large Vision-Language Models

Large Vision-Language Models (LVLMs) have exhibited impressive capabilities across various visual tasks, yet they remain hindered by the persistent ...

DO-RAG: A Domain-Specific QA Framework Using Knowledge Graph-Enhanced Retrieval-Augmented Generation

Domain-specific QA systems require not just generative fluency but high factual accuracy grounded in structured expert knowledge. While recent Retri...

Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning?

The 180x360 omnidirectional field of view captured by 360-degree cameras enables their use in a wide range of applications such as embodied AI and v...