Schizophrenia

Hallucination at a Glance: Controlled Visual Edits and Fine-Grained Multimodal Learning

Multimodal large language models (MLLMs) have achieved strong performance on vision-language tasks but still struggle with fine-grained visual differences, leading to hallucinations or missed semantic shifts. We attribute this to limitations in both training data and learning objectives. To address these issues, we propose a controlled data generation pipeline that produces minimally edited imag...

Mitigating Behavioral Hallucination in Multimodal Large Language Models for Sequential Images

While multimodal large language models excel at various tasks, they still suffer from hallucinations, which limit their reliability and scalability for broader domain applications. To address this issue, recent research mainly focuses on objective hallucination. However, for sequential images, besides objective hallucination, there is also behavioral hallucination, which is less studied. This wo...

Interpretable and Reliable Detection of AI-Generated Images via Grounded Reasoning in MLLMs

The rapid advancement of image generation technologies intensifies the demand for interpretable and robust detection methods. Although existing appr...

Mitigating Object Hallucination via Robust Local Perception Search

Recent advancements in Multimodal Large Language Models (MLLMs) have enabled them to effectively integrate vision and language, addressing a variety...

\textit{QuantMCP}: Grounding Large Language Models in Verifiable Financial Reality

Large Language Models (LLMs) hold immense promise for revolutionizing financial analysis and decision-making, yet their direct application is often ...

AssetDropper: Asset Extraction via Diffusion Models with Reward-Driven Optimization

Recent research on generative models has primarily focused on creating product-ready visual outputs; however, designers often favor access to standa...

Precise Information Control in Long-Form Text Generation

A central challenge in modern language models (LMs) is intrinsic hallucination: the generation of information that is plausible but unsubstantiated ...

On Quantum Random Walks in Biomolecular Networks

Biomolecular networks, such as protein-protein interactions, gene-gene associations, and cell-cell interactions, offer valuable insights into the co...

Zero-Shot Event Causality Identification via Multi-source Evidence Fuzzy Aggregation with Large Language Models

Event Causality Identification (ECI) aims to detect causal relationships between events in textual contexts. Existing ECI models predominantly rely ...

High Accuracy, Less Talk (HALT): Reliable LLMs through Capability-Aligned Finetuning

Large Language Models (LLMs) currently respond to every prompt. However, they can produce incorrect answers when they lack knowledge or capability -...

Mitigating Hallucinations in Large Vision-Language Models via Entity-Centric Multimodal Preference Optimization

Large Visual Language Models (LVLMs) have demonstrated impressive capabilities across multiple tasks. However, their trustworthiness is often challe...

Tomographic Foundation Model -- FORCE: Flow-Oriented Reconstruction Conditioning Engine

Computed tomography (CT) is a major medical imaging modality. Clinical CT scenarios, such as low-dose screening, sparse-view scanning, and metal imp...

Aligning VLM Assistants with Personalized Situated Cognition

Vision-language models (VLMs) aligned with general human objectives, such as being harmless and hallucination-free, have become valuable assistants ...

The evolving field of digital mental health: current evidence and implementation issues for smartphone apps, generative artificial intelligence, and virtual reality.

The expanding domain of digital mental health is transitioning beyond traditional telehealth to incorporate smartphone apps, virtual reality, and gene...

A case study on generative artificial intelligence to extract the fundamental sleep parameters from polysomnography notes.

UNLABELLED: Generative artificial intelligence utilizing transformer technology is widely seen as a groundbreaking advancement in applied artificial i...

Fact-Controlled Diagnosis of Hallucinations in Medical Text Summarization

Hallucinations in large language models (LLMs) during summarization of patient-clinician dialogues pose significant risks to patient care and clinic...

Chain-of-Frames: Advancing Video Understanding in Multimodal LLMs via Frame-Aware Reasoning

Recent work has shown that eliciting Large Language Models (LLMs) to generate reasoning traces in natural language before answering the user's reque...

Improving Reliability and Explainability of Medical Question Answering through Atomic Fact Checking in Retrieval-Augmented LLMs

Large language models (LLMs) exhibit extensive medical knowledge but are prone to hallucinations and inaccurate citations, which pose a challenge to...

MIRAGE: Assessing Hallucination in Multimodal Reasoning Chains of MLLM

Multimodal hallucination in multimodal large language models (MLLMs) restricts the correctness of MLLMs. However, multimodal hallucinations are mult...

Preemptive Hallucination Reduction: An Input-Level Approach for Multimodal Language Model

Visual hallucinations in Large Language Models (LLMs), where the model generates responses that are inconsistent with the visual input, pose a signi...