Latest AI and machine learning research in schizophrenia for healthcare professionals.
We present MedHal, a novel large-scale dataset specifically designed to evaluate if models can detect hallucinations in medical texts. Current hallucination detection methods face significant limitations when applied to specialized domains like medicine, where they can have disastrous consequences. Existing medical datasets are either too small, containing only a few hundred samples, or focus on...
Generative AI is increasingly used in scientific domains, from protein folding to climate modeling. But these models produce distinctive errors known as hallucinations - outputs that are incorrect yet superficially plausible. Worse, some arguments suggest that hallucinations are an inevitable consequence of the mechanisms underlying generative inference. Fortunately, such arguments rely on a con...
Fine-grained domain generalization (FGDG) aims to learn a fine-grained representation that can be well generalized to unseen target domains when onl...
Although multimodal large language models (MLLMs) exhibit remarkable reasoning capabilities on complex multimodal understanding tasks, they still su...
We present a perception in reflection paradigm designed to transcend the limitations of current large vision-language models (LVLMs), which are expe...
This paper introduces a comprehensive system for detecting hallucinations in large language model (LLM) outputs in enterprise settings. We present a...
Large Vision-Language Models have demonstrated remarkable performance across various tasks; however, the challenge of hallucinations constrains thei...
Motivated by recent improvements in generative AI and wearable camera devices (e.g. smart glasses and AI-enabled pins), I investigate the ability of...
With the rapid development of 3D printing, the demand for personalized and customized production on the manufacturing line is steadily increasing. E...
Text-to-video (T2V) generation has made tremendous progress in generating complicated scenes based on texts. However, human-object interaction (HOI)...
This article surveys Evaluation models to automatically detect hallucinations in Retrieval-Augmented Generation (RAG), and presents a comprehensive ...
The rapid development of multimodal large language models has resulted in remarkable advancements in visual perception and understanding, consolidat...
Multimodal large language models (MLLMs) have demonstrated significant potential in medical Visual Question Answering (VQA). Yet, they remain prone ...
Recent advances in large language models have highlighted the critical need for precise control over model outputs through predefined constraints. W...
Disorganized thinking is a key diagnostic indicator of schizophrenia-spectrum disorders. Recently, clinical estimates of the severity of disorganize...
The rapid advancement of large vision-language models (LVLMs) has driven significant progress in multimodal tasks, enabling models to interpret, rea...
The hallucination of large multimodal models (LMMs), providing responses that appear correct but are actually incorrect, limits their reliability an...
This study addresses the technical bottlenecks in handling long text and the "hallucination" issue caused by insufficient short text information in ...
Composed image retrieval (CIR) enables users to search images using a reference image combined with textual modifications. Recent advances in vision...
Evaluating generative foundation models on open-ended multimodal understanding (MMU) and generation (MMG) tasks across diverse modalities (e.g., ima...