Nivedita joshi saraf biography of rory

  • Life with Nivedita Joshi Saraf and their son.
  • We introduce RULEARN, a novel benchmark specifically designed to assess the rule-learning abilities of LLM agents in interactive settings.
  • PDF | Training of large language models (LLMs) is typically distributed across a large number of accelerators to reduce training time.

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    CRANE

    Long-Term Association of Low-Density Lipoprotein Cholesterol With Cardiovascular Mortality in Individuals at Low 10-Year Risk of Atherosclerotic Cardiovascular Disease

    Shuaib M. Abdullah, Laura F. Defina, David Leonard, Carolyn E. Barlow, Nina B. Radford, Benjamin L. Willis, Anand Rohatgi, Darren K. McGuire, James A. de Lemos, Scott M. Grundy, Jarett D. Berry, Amit Khera
    2018 - 10.1161/CIRCULATIONAHA.118.034273 - Circulation

    Interpretation of the evidence for the efficacy and safety of statin therapy

    Rory Collins, Christina Reith, Jonathan Emberson, Jane Armitage, Colin Baigent, Lisa Blackwell, Roger Blumenthal, John Danesh, George Davey Smith, David DeMets, Stephen Evans, Malcolm Law, Stephen MacMahon, Seth Martin, Bruce Neal, Neil Poulter, David Preiss, Paul Ridker, Ian Roberts, Anthony Rodgers, Peter Sandercock, Kenneth Schulz, Peter Sever, John Simes, Liam Smeeth, Nicholas Wald, Salim Yusuf, Richard Peto
    2016 - 10.1016/S0140-6736(16)31357-5 - The Lancet

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    IDEA: Enhancing the Rule Learning Ability of Large Language Model Agent through Induction, Deduction, and Abduction

    Kaiyu He, Mian Zhang, Shuo Yan, Peilin Wu, Zhiyu Zoey Chen
    Department of Computer Science
    University of Texas at Dallas
    {kaiyu.he, zhiyu.chen2}@utdallas.edu

    Abstract

    While large language models (LLMs) have been thoroughly evaluated for deductive and inductive reasoning, their proficiency in abductive reasoning and holistic rule learning in interactive environments remains less explored. We introduce RULEARN, a novel benchmark specifically designed to assess the rule-learning abilities of LLM agents in interactive settings. In RULEARN, agents strategically interact with simulated environments to gather observations, discern patterns, and solve complex problems. To enhance the rule-learning capabilities for LLM agents, we propose IDEA, a novel reasoning framework that integrates the process of Induction, DEduction, and Abduction. The IDEA agent generates initial h

  • nivedita joshi saraf biography of rory

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