2 citations · 5 across the 6 of their papers we have counts for
6 papers
Evaluating Large Language Models on Misconceptions in Multi-Turn Medical Conversations
Monica Munnangi, Saiph Savage
Patients seeking medical information often ask questions that embed incorrect assumptions or misconceptions. In such cases, safe medical communication requires not only answering t…
ThReadMed-QA: A Multi-Turn Medical Dialogue Benchmark from Real Patient Questions
Monica Munnangi, Saiph Savage
Medical question-answering benchmarks predominantly evaluate single-turn exchanges, failing to capture the iterative, clarification-seeking nature of real patient consultations. We…
FactEHR: A Dataset for Evaluating Factuality in Clinical Notes Using LLMs
Monica Munnangi, Akshay Swaminathan, Jason Alan Fries +8
Verifying and attributing factual claims is essential for the safe and effective use of large language models (LLMs) in healthcare. A core component of factuality evaluation is fac…
A Brief History of Named Entity Recognition
Monica Munnangi
A large amount of information in today's world is now stored in knowledge bases. Named Entity Recognition (NER) is a process of extracting, disambiguation, and linking an entity fr…
Open (Clinical) LLMs are Sensitive to Instruction Phrasings
Alberto Mario Ceballos Arroyo, Monica Munnangi, Jiuding Sun +4
Instruction-tuned Large Language Models (LLMs) can perform a wide range of tasks given natural language instructions to do so, but they are sensitive to how such instructions are p…
On-the-fly Definition Augmentation of LLMs for Biomedical NER
Monica Munnangi, Sergey Feldman, Byron C Wallace +3
Despite their general capabilities, LLMs still struggle on biomedical NER tasks, which are difficult due to the presence of specialized terminology and lack of training data. In th…