2 citations · 3 across the 6 of their papers we have counts for
6 papers · 1 filter
From Small to Large: Assessing Medical Reasoning, Benchmark Contamination, and Model Size in Language Models
Navapat Nananukul, Mayank Kejriwal
Large language models (LLMs) now answer medical exam questions at or above expert level, but it is unclear whether such scores reflect clinical competence, or are instead affected…
ClinicBot: A Guideline-Grounded Clinical Chatbot with Prioritized Evidence RAG and Verifiable Citations
Navapat Nananukul, Mayank Kejriwal
Clinical diagnosis requires answers that are accurate, verifiable, and explicitly grounded in official guidelines. While large language models excel at natural language processing,…
An Analysis of Artificial Intelligence Adoption in NIH-Funded Research
Navapat Nananukul, Mayank Kejriwal
Understanding the landscape of artificial intelligence (AI) and machine learning (ML) adoption across the National Institutes of Health (NIH) portfolio is critical for research fun…
LOGicalThought: Logic-Based Ontological Grounding of LLMs for High-Assurance Reasoning
Navapat Nananukul, Yue Zhang, Ryan Lee +5
High-assurance reasoning, particularly in critical domains such as law and medicine, requires conclusions that are accurate, verifiable, and explicitly grounded in evidence. This r…
HALO: An Ontology for Representing and Categorizing Hallucinations in Large Language Models
Navapat Nananukul, Mayank Kejriwal
Recent progress in generative AI, including large language models (LLMs) like ChatGPT, has opened up significant opportunities in fields ranging from natural language processing to…
Cost-Efficient Prompt Engineering for Unsupervised Entity Resolution
Navapat Nananukul, Khanin Sisaengsuwanchai, Mayank Kejriwal
Entity Resolution (ER) is the problem of semi-automatically determining when two entities refer to the same underlying entity, with applications ranging from healthcare to e-commer…