Khanh X. Nguyen

khanh.nguyen AT

I am a postdoc researcher in the NLP group at Princeton University. I am grateful to work with Prof. Karthik Narasimhan and his awesome students. My research focuses on various topics of Human-AI Communcation. I deeply care about making AI agents beneficial while retaining human control on them. The premises of my research are:

  • Effective communication with humans magnifies AI capability while reducing its risk;
  • Communication is not merely about imitating human language. It is about how to express thoughts, comprehend intentions, and accomplishing goals through (any) language.

My work aims to push forward three directions:

  • Learning from natural human feedback: Traditional learning frameworks employ primitive, idealized learning signals as communication media, thus limiting the ability of AI agents to learn directly from humans. How can we enable AI agents to improve themselves given diverse types of learning signals that humans can provide? Towards this goal, I have built agents that learn from noisy ratings [EMNLP’17] and language descriptions [ICML’21].
  • Learning to express uncertainties: It is a mistake to think that only humans should ask AI for help and not the reverse case. By asking a question, an agent can: (i) express its uncertainties (not just uncertainty), (ii) obtain information to expand its task-solving capabilities. So more safety and more utility! But how to teach agents when and what to ask? I author a series of papers which develop methods and highlight the challenges of this problem [EMNLP’15’, CVPR’19, EMNLP’19, ICML’22].
  • Probing and modeling human cognitive capabilities: Goal-regulated models like ChatGPT is better at comprehending humans than behavior-cloned models like GPT. One possible hypothesis is that the former behaves more like a human: by optimizing for an internal reward function, it thinks about “what to achieve in the long term?” rather than “what do in the next moment?” Are there more aspects about the human cognitive system that can inspire useful principles for developing AI? Which human cognitive capabilities that AI agents lack? Which would empower AI and which would be redundant? Which can be learned and which need to be built? I investigate some of these questions in the context of instruction generation [PIGen’23].

Facts about me:

  • I obtained my PhD at the University of Maryland–College Park, advised by the awesome Hal Daumé III.
  • My real name is Nguyễn Xuân Khánh :loud_sound:. My first name is usually confused with Khan or Kahn :(
  • I was born in Việt Nam :vietnam:, a peaceful country (click here for inspiration to visit us).
  • I am also proud to be a PTNK (Phổ Thông Năng Khiếu) alumnus.

student mentees

(ordered alphabetically)


Dec 20, 2022 New paper on cognitive evaluation for instruction generation agents. TL;DR They need better theory-of-mind capabilities.
Aug 17, 2022 I will be organizing InterNLP workshop at NeurIPS 2022. Please submit your papers if interested!

selected publications

  1. Help, Anna! Visual Navigation with Natural Multimodal Assistance via Retrospective Curiosity-Encouraging Imitation Learning
    Khanh Nguyen, and Hal Daumé III
    In EMNLP, 2019
  2. Interactive Learning from Activity Description
    Khanh Nguyen, Dipendra Misra, Robert Schapire, and 2 more authors
    In ICML, 2021
  3. Posterior calibration and exploratory analysis for natural language processing models
    Khanh Nguyen, and Brendan O’Connor
    In EMNLP, Sep 2015
  4. Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback
    Khanh Nguyen, Hal Daumé III, and Jordan Boyd-Graber
    In EMNLP, Sep 2017
  5. A Framework for Learning to Request Rich and Contextually Useful Information from Humans
    Khanh Nguyen, Yonatan Bisk, and Hal Daumé III
    In ICML, Jul 2022