3 citations · 3 across the 4 of their papers we have counts for
5 papers
Do AI chatbots find what experts would? Effects of model, user role, and sample size on study retrieval for medical questions
Qingfang Liu, Qiao Jin, Joe D. Menke +2
Large language model (LLM) chatbots are increasingly used to answer clinical questions with citations to relevant studies, yet the quality of retrieved evidence and factors influen…
Robust Biomedical Publication Type and Study Design Classification with Knowledge-Guided Perturbations
Shufan Ming, Joe D. Menke, Neil R. Smalheiser +1
Accurately and consistently indexing biomedical literature by publication type and study design is essential for supporting evidence synthesis and knowledge discovery. Prior work o…
Divide-Prompt-Refine: a Training-Free, Structure-Aware Framework for Biomedical Abstract Generation
Sylvey Lin, Joe Menke, Shufan Ming +3
Biomedical abstracts play a critical role in downstream NLP applications, such as information retrieval, biocuration, and biomedical knowledge discovery. However, a non-trivial num…
Goals and Strategies for the Indexing of Publication Types and Study Designs
Neil R. Smalheiser, Joe D. Menke, Arthur W. Holt +2
Objectives. Major research and implementation efforts have been devoted to indexing articles according to the major topics discussed, but much less effort to indexing their publica…
Jailbreaking Large Language Models Against Moderation Guardrails via Cipher Characters
Haibo Jin, Andy Zhou, Joe D. Menke +1
Large Language Models (LLMs) are typically harmless but remain vulnerable to carefully crafted prompts known as ``jailbreaks'', which can bypass protective measures and induce harm…