arXiv:2401.05827
공식 코드 · Papers with Code
Hallucination Benchmark in Medical Visual Question Answering
원문 초록
The recent success of large language and vision models (LLVMs) on vision question answering (VQA), particularly their applications in medicine (Med-VQA), has shown a great potential of realizing effective visual assistants for healthcare. However, these models are not extensively tested on the hallucination phenomenon in clinical settings. Here, we created a hallucination benchmark of medical images paired with question-answer sets and conducted a comprehensive evaluation of the state-of-the-art models. The study provides an in-depth analysis of current models' limitations and reveals the effectiveness of various prompting strategies.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
SMMILE: An Expert-Driven Benchmark for Multimodal Medical In-Context Learning
GEMeX-ThinkVG: Towards Thinking with Visual Grounding in Medical VQA via Reinforcement Learning
CAPO: Reinforcing Consistent Reasoning in Medical Decision-Making
HalLoc: Token-level Localization of Hallucinations for Vision Language Models
Kvasir-VQA-x1: A Multimodal Dataset for Medical Reasoning and Robust MedVQA in Gastrointestinal Endoscopy
OVQA: A Clinically Generated Visual Question Answering Dataset
토론
이 논문과 연결된 포럼 글이 아직 없습니다.