20 citations · 35 across the 10 of their papers we have counts for
8 papers · 1 filter
SearchRAG: Can Search Engines Be Helpful for LLM-based Medical Question Answering?
Yucheng Shi, Tianze Yang, Canyu Chen +4
Large Language Models (LLMs) have shown remarkable capabilities in general domains but often struggle with tasks requiring specialized knowledge. Conventional Retrieval-Augmented G…
ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?
Canyu Chen, Jian Yu, Shan Chen +8
Large Language Models (LLMs) hold great promise to revolutionize current clinical systems for their superior capacities on medical text processing tasks and medical licensing exams…
Can Knowledge Editing Really Correct Hallucinations?
Baixiang Huang, Canyu Chen, Xiongxiao Xu +2
Large Language Models (LLMs) suffer from hallucinations, referring to the non-factual information in generated content, despite their superior capacities across tasks. Meanwhile, k…
Model Attribution in LLM-Generated Disinformation: A Domain Generalization Approach with Supervised Contrastive Learning
Alimohammad Beigi, Zhen Tan, Nivedh Mudiam +3
Model attribution for LLM-generated disinformation poses a significant challenge in understanding its origins and mitigating its spread. This task is especially challenging because…
Can Editing LLMs Inject Harm?
Canyu Chen, Baixiang Huang, Zekun Li +12
Large Language Models (LLMs) have emerged as a new information channel. Meanwhile, one critical but under-explored question is: Is it possible to bypass the safety alignment and in…
Introducing v0.5 of the AI Safety Benchmark from MLCommons
Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed +97
This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safe…