Bhargavi narayan biography of rory

  • Lina Solavita, an investigator for Pritzger Insurance, moved to Knockemout, Virginia, where she became entangled in a complicated situation.
  • Massively Multilingual Language Models (MMLMs) like mBERT, XLMR and XY-LENT support around 100 languages of the world.
  • Long-context language models (LCLMs) unlock a myriad of applications, from summarizing long documents to learning new tasks on the fly with.

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    Profile

    • Clinical Assistant Professor in Oral and Maxillofacial Surgery
      口腔頜面外科臨床助理教授
    Oral and Maxillofacial Surgery
    • Computer-assisted surgery
    • Oral cancer
    • Jaw reconstruction

      Article

    • Pu Jane J., Yu Xingna, Pow Edmond H.N., Lam Walter Y.H., Su Yu-Xiong. Single-Double-Single Barrel (1-2-1) Fibula Free Flap Design for Functional and Esthetic Brown Class III Mandibular Reconstruction , Plastic and Reconstructive Surgery 2025; doi:10.1097/PRS.0000000000011950
    • Callahan Nicholas, Pu Jane Jingya, Su Yu-Xiong Richard, Zbarsky Steven JD, Weyh Ashleigh, Viet Chi T. Benefits and Controversies of Midface and Maxillary Reconstruction, Atlas of The Oral and Maxillofacial Surgery Clinics of North America 2024; doi:10.1016/j.cxom.2023.12.006
    • Pu Jingya Jane, Choi Wing Shan, Wong May CM, Wu Songying, Leung Pui Hang, Yang Wei-fa, Su Yu-Xiong. Long-term stability of jaw reconstruction with microvascular bone flaps: A prospective longitudinal study, Oral Oncology 2024; 152 doi:1

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    • bhargavi narayan biography of rory

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      HELMET: How to Evaluate Long-Context Language Models Effectively and Thoroughly

      Howard Yen  Tianyu Gao  Minmin Hou  Ke Ding
      Daniel FleischerPeter IzasakMoshe WasserblatDanqi Chen
      p Princeton Language and Intelligence, Princeton University iIntel
      {hyen,tianyug,danqic}@cs.princeton.edu

      Abstract

      There have been many benchmarks for evaluating long-context language models (LCLMs), but developers often rely on synthetic tasks like needle-in-a-haystack (NIAH) or arbitrary subsets of tasks. It remains unclear whether they translate to the diverse downstream applications of LCLMs, and the inconsistency further complicates model comparison. We investigate the underlying reasons behind current practices and find that existing benchmarks often provide noisy signals due to low coverage of applications, insufficient lengths, unreliable metrics, and incompatibility with base models. In this work, we present HELMET (How to Evaluate Long-context Models Effectively and Thoroughly), a compre