Papers by Monica Agrawal
Large language models are few-shot clinical information extractors (2022.emnlp-main)
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| Challenge: | a long-running goal of clinical NLP is the extraction of important variables trapped in clinical notes. |
| Approach: | They propose to use large language models to tackle diverse clinical extraction tasks . they propose to reannote existing CASI datasets to compare their models with clinical text. |
| Outcome: | The proposed models outperform existing models on few-shot clinical information extraction tasks. |
MedRedFlag: Investigating how LLMs Redirect Misconceptions in Real-World Health Communication (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are increasingly being used by lay users for medical advice, but they have not yet been tested for this crucial competency. |
| Approach: | They develop a semi-automated pipeline to curate MedRedFlag, a dataset of 1100+ reddit questions that require redirection. |
| Outcome: | The proposed pipeline compares state-of-the-art LLMs to those from clinicians to find out how they perform under real-world health communication. |
“What’s Up, Doc?”: Analyzing How Users Seek Health Information in Large-Scale Conversational AI Datasets (2025.findings-emnlp)
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Akshay Paruchuri, Maryam Aziz, Rohit Vartak, Ayman Ali, Best Uchehara, Xin Liu, Ishan Chatterjee, Monica Agrawal
| Challenge: | a growing number of people are seeking healthcare information from large language models via chatbots, yet the nature and inherent risks of these interactions remain unexplored. |
| Approach: | They use a curated dataset of 11K real-world conversations composed of 25K user messages to analyze user interactions across 21 health specialties. |
| Outcome: | The proposed dataset consists of 11K real-world conversations composed of 25K user messages. |