11 citations · 13 across the 5 of their papers we have counts for
6 papers
Can Factual Opinions Be Edited (Manipulated) in Large Language Models?
Yuanpu Cao, Ziyi Yin, Fenglong Ma +1
Large Language Models (LLMs) are increasingly integrated into various domains, making knowledge editing techniques crucial yet potentially hazardous. Current editing methods primar…
MedSIGHT: Towards Grounded Visual Comprehension in Medical Large Vision-Language Models
Aofei Chang, Le Huang, Alex James Boyd +4
Medical large vision-language models (Med-LVLMs) have recently achieved remarkable progress in vision-language comprehension and medical image segmentation. However, existing model…
Focus on What Matters: Enhancing Medical Vision-Language Models with Automatic Attention Alignment Tuning
Aofei Chang, Le Huang, Alex James Boyd +4
Medical Large Vision-Language Models (Med-LVLMs) often exhibit suboptimal attention distribution on visual inputs, leading to hallucinated or inaccurate outputs. Existing mitigatio…
MedHEval: Benchmarking Hallucinations and Mitigation Strategies in Medical Large Vision-Language Models
Aofei Chang, Le Huang, Parminder Bhatia +3
Large Vision Language Models (LVLMs) are becoming increasingly important in the medical domain, yet Medical LVLMs (Med-LVLMs) frequently generate hallucinations due to limited expe…
CoRelation: Boosting Automatic ICD Coding Through Contextualized Code Relation Learning
Junyu Luo, Xiaochen Wang, Jiaqi Wang +3
Automatic International Classification of Diseases (ICD) coding plays a crucial role in the extraction of relevant information from clinical notes for proper recording and billing.…
Zero-Resource Hallucination Prevention for Large Language Models
Junyu Luo, Cao Xiao, Fenglong Ma
The prevalent use of large language models (LLMs) in various domains has drawn attention to the issue of "hallucination," which refers to instances where LLMs generate factually in…