activity
20202024
most citedWILDS: A Benchmark of in-the-Wild Distribution Shifts

285 citations · 290 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2024★ 1 cited

JPEG-LM: LLMs as Image Generators with Canonical Codec Representations

Xiaochuang Han, Marjan Ghazvininejad, Pang Wei Koh +1

Recent work in image and video generation has been adopting the autoregressive LLM architecture due to its generality and potentially easy integration into multi-modal systems. The…

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.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…

cs.LG2020★ 285 cited

WILDS: A Benchmark of in-the-Wild Distribution Shifts

Pang Wei Koh, Shiori Sagawa, Henrik Marklund +20

Distribution shifts -- where the training distribution differs from the test distribution -- can substantially degrade the accuracy of machine learning (ML) systems deployed in the…