Schizophrenia

Evaluating Hallucination in Text-to-Image Diffusion Models with Scene-Graph based Question-Answering Agent

Contemporary Text-to-Image (T2I) models frequently depend on qualitative human evaluations to assess the consistency between synthesized images and the text prompts. There is a demand for quantitative and automatic evaluation tools, given that human evaluation lacks reproducibility. We believe that an effective T2I evaluation metric should accomplish the following: detect instances where the gen...

TOBUGraph: Knowledge Graph-Based Retrieval for Enhanced LLM Performance Beyond RAG

Retrieval-Augmented Generation (RAG) is one of the leading and most widely used techniques for enhancing LLM retrieval capabilities, but it still faces significant limitations in commercial use cases. RAG primarily relies on the query-chunk text-to-text similarity in the embedding space for retrieval and can fail to capture deeper semantic relationships across chunks, is highly sensitive to chun...

Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

We introduce InternVL 2.5, an advanced multimodal large language model (MLLM) series that builds upon InternVL 2.0, maintaining its core model archi...

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion

We present Florence-VL, a new family of multimodal large language models (MLLMs) with enriched visual representations produced by Florence-2, a gene...

Deep priors for satellite image restoration with accurate uncertainties

Satellite optical images, upon their on-ground receipt, offer a distorted view of the observed scene. Their restoration, classically including denoi...

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis

Recent advancements in large vision-language models (LVLM) have significantly enhanced their ability to comprehend visual inputs alongside natural l...

An Evolutionary Large Language Model for Hallucination Mitigation

The emergence of LLMs, like ChatGPT and Gemini, has marked the modern era of artificial intelligence applications characterized by high-impact appli...

CC-OCR: A Comprehensive and Challenging OCR Benchmark for Evaluating Large Multimodal Models in Literacy

Large Multimodal Models (LMMs) have demonstrated impressive performance in recognizing document images with natural language instructions. However, ...

AI Benchmarks and Datasets for LLM Evaluation

LLMs demand significant computational resources for both pre-training and fine-tuning, requiring distributed computing capabilities due to their lar...

Automating Feedback Analysis in Surgical Training: Detection, Categorization, and Assessment

This work introduces the first framework for reconstructing surgical dialogue from unstructured real-world recordings, which is crucial for characte...

A multimodal vision transformer for interpretable fusion of functional and structural neuroimaging data.

Multimodal neuroimaging is an emerging field that leverages multiple sources of information to diagnose specific brain disorders, especially when deep...

Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity

Evaluating the importance of different layers in large language models (LLMs) is crucial for optimizing model performance and interpretability. This...

Leveraging Vision-Language Models for Manufacturing Feature Recognition in CAD Designs

Automatic feature recognition (AFR) is essential for transforming design knowledge into actionable manufacturing information. Traditional AFR method...

RadFlag: A Black-Box Hallucination Detection Method for Medical Vision Language Models

Generating accurate radiology reports from medical images is a clinically important but challenging task. While current Vision Language Models (VLMs...

Making the most of errors: Utilizing erroneous classifications generated by machine-learning models of neuroimaging data to capture disorder heterogeneity.

Within-disorder heterogeneity complicates mapping the neurobiological features of psychopathology to Diagnostic and Statistical Manual of Mental Disor...

MaCTG: Multi-Agent Collaborative Thought Graph for Automatic Programming

With the rapid advancement of Large Language Models (LLMs), LLM-based approaches have demonstrated strong problem-solving capabilities across variou...

Privacy-hardened and hallucination-resistant synthetic data generation with logic-solvers

Machine-generated data is a valuable resource for training Artificial Intelligence algorithms, evaluating rare workflows, and sharing data under str...

Reducing Hallucinations in Vision-Language Models via Latent Space Steering

Hallucination poses a challenge to the deployment of large vision-language models (LVLMs) in applications. Unlike in large language models (LLMs), h...

Magnifier Prompt: Tackling Multimodal Hallucination via Extremely Simple Instructions

Hallucinations in multimodal large language models (MLLMs) hinder their practical applications. To address this, we propose a Magnifier Prompt (MagP...

Artificial Intelligence in the Legal Field: Law Students Perspective

The Artificial Intelligence field, or AI, experienced a renaissance in the last few years across various fields such as law, medicine, and finance. ...