publications by categories in reversed chronological order.

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  1. Benjamin Plaut, Khanh Nguyen, and Tu Trinh
    ArXiv, 2024

    💡 LLM probabilities are predictive of the correctness of their answers on Q&A tasks

  2. Alex Zhang, Albert Lin, Jens Tuyls, Khanh Nguyen, and Karthik Narasimhan
    ArXiv, 2024

    💡 Models that can read texts to simulate environments


  1. Ruijie Zheng, Khanh Nguyen, Furong Huang, Hal Daumé III, and Karthik Narasimhan
    Workshop on Intrinsically Motivated Open-ended Learning (NeurIPS), 2023

    💡 Learn increasingly abstract language to reduce communication effort

  2. Lingjun Zhao, Khanh Nguyen, and Hal Daumé III
    EMNLP Findings, 2023

    💡 Detect hallucinations in model-generated navigation instructions

  3. Khanh Nguyen
    Workshop on Theory of Mind (ICML), 2023

    💡 LLMs as limited communicative agents and how to augment them

  4. Lingjun Zhao, Khanh Nguyen, and Hal Daumé III
    ACL Findings, 2023

    💡 Improve instruction generation by predicting human interpretation


  1. Khanh Nguyen, Yonatan Bisk, and Hal Daumé III
    ICML, Jul 2022

    💡 Agents that can decide when and what question to ask, and incoporate answer to make progress


  1. Khanh Nguyen, Dipendra Misra, Robert Schapire, Miro Dudı́k, and Patrick Shafto
    ICML, Jul 2021

    💡 Framework for learning from language feedback with theoretical guarantees


  1. Khanh Nguyen, and Hal Daumé III
    EMNLP, 2019

    💡 Agents that can ask for help and interpret language instructions

  2. Khanh Nguyen, Debadeepta Dey, Chris Brockett, and Bill Dolan
    CVPR, 2019

    💡 Agent that can ask for help

  3. Khanh Nguyen, and Hal Daumé III
    New Frontiers in Summarization Workshop at EMNLP, Nov 2019

    💡 Evaluation dataset for cross-lingual summarization


  1. Khanh Nguyen, Hal Daumé III, and Jordan Boyd-Graber
    EMNLP, Sep 2017

    💡 Improve machine translation with reinforcement learning from noisy ratings

  2. Amr Sharaf, Shi Feng, Khanh Nguyen, Kiante Brantley, and Hal Daumé III
    WMT, Sep 2017


  1. Khanh Nguyen, and Brendan O’Connor
    EMNLP, Sep 2015

    💡 Calibrated probabilities for detecting errors of NLP models