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researcher

Joëlle Barral

4 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author3

Across the 3 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CL3
  • cs.AI1
ORCID 0009-0009-0432-5148

identity via Semantic Scholar / OpenAlex

most citedTowards Expert-Level Medical Question Answering with Large Language Models

335 citations · 701 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CL2024★ 238 cited

Gemma: Open Models Based on Gemini Research and Technology

Gemma Team, Thomas Mesnard, Cassidy Hardin +105

This work introduces Gemma, a family of lightweight, state-of-the art open models built from the research and technology used to create Gemini models. Gemma models demonstrate stro…

cs.AI2024★ 100 cited

Towards Conversational Diagnostic AI

Tao Tu, Anil Palepu, Mike Schaekermann +22

At the heart of medicine lies the physician-patient dialogue, where skillful history-taking paves the way for accurate diagnosis, effective management, and enduring trust. Artifici…

cs.CL2023★ 28 cited

Towards Generalist Biomedical AI

Tao Tu, Shekoofeh Azizi, Danny Driess +29

Medicine is inherently multimodal, with rich data modalities spanning text, imaging, genomics, and more. Generalist biomedical artificial intelligence (AI) systems that flexibly en…

cs.CL2023★ 335 cited

Towards Expert-Level Medical Question Answering with Large Language Models

Karan Singhal, Tao Tu, Juraj Gottweis +28

Recent artificial intelligence (AI) systems have reached milestones in "grand challenges" ranging from Go to protein-folding. The capability to retrieve medical knowledge, reason o…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.