activity
20232025
most citedParticipation in the age of foundation models

48 citations · 55 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CL2025

What's In My Human Feedback? Learning Interpretable Descriptions of Preference Data

Rajiv Movva, Smitha Milli, Sewon Min +1

Human feedback can alter language models in unpredictable and undesirable ways, as practitioners lack a clear understanding of what feedback data encodes. While prior work studies…

cs.LG2025

Evaluating multiple models using labeled and unlabeled data

Divya Shanmugam, Shuvom Sadhuka, Manish Raghavan +3

It remains difficult to evaluate machine learning classifiers in the absence of a large, labeled dataset. While labeled data can be prohibitively expensive or impossible to obtain,…

cs.LG2024★ 3 cited

Generative AI in Medicine

Divya Shanmugam, Monica Agrawal, Rajiv Movva +4

The increased capabilities of generative AI have dramatically expanded its possible use cases in medicine. We provide a comprehensive overview of generative AI use cases for clinic…

cs.CL2024

Annotation alignment: Comparing LLM and human annotations of conversational safety

Rajiv Movva, Pang Wei Koh, Emma Pierson

Do LLMs align with human perceptions of safety? We study this question via annotation alignment, the extent to which LLMs and humans agree when annotating the safety of user-chatbo…

cs.CL2024★ 4 cited

MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoning

Shuyue Stella Li, Vidhisha Balachandran, Shangbin Feng +4

Users typically engage with LLMs interactively, yet most existing benchmarks evaluate them in a static, single-turn format, posing reliability concerns in interactive scenarios. We…

cs.CY2024★ 48 cited

Participation in the age of foundation models

Harini Suresh, Emily Tseng, Meg Young +3

Growing interest and investment in the capabilities of foundation models has positioned such systems to impact a wide array of public services. Alongside these opportunities is the…

cs.LG2024

Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium

Hyewon Jeong, Sarah Jabbour, Yuzhe Yang +40

The third ML4H symposium was held in person on December 10, 2023, in New Orleans, Louisiana, USA. The symposium included research roundtable sessions to foster discussions between…

cs.CY2023

Using large language models to promote health equity

Emma Pierson, Divya Shanmugam, Rajiv Movva +12

Advances in large language models (LLMs) have driven an explosion of interest about their societal impacts. Much of the discourse around how they will impact social equity has been…