Nelson Liu

nfliu@cs.stanford.edu
Gates 318

Nelson Liu

I am a fifth-year PhD candidate in computer science at Stanford University, where I work in the Natural Language Processing Group. I am advised by Percy Liang, and my research is graciously supported by a NSF Graduate Research Fellowship. These days, I'm mostly interested in building practically-useful NLP systems, especially for information-seeking applications.

Previously, I received undergraduate degrees in computer science and linguistics from the University of Washington, where I worked with Noah A. Smith. I've also spent time at Samaya AI, Google Research, the Allen Institute for Artificial Intelligence (AI2), and the USC Information Sciences Institute.

I owe a lot to the patience of several research mentors who were willing to spend their own time showing me the ropes when I was getting started. If you're interested in getting started in NLP research and think it'd be useful to chat, please feel free to email me.

[Full CV] [Github] [Google Scholar] [Blog] [Twitter]


Publications

  • Lost in the Middle: How Language Models Use Long Contexts
    Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang.
    In Transactions of the Association for Computational Linguistics (TACL), 2023.
    [bib] [code] [poster] [abstract]

  • Anchor Prediction: Automatic Refinement of Internet Links
    Nelson F. Liu, Kenton Lee, and Kristina Toutanova.
    Preprint, 2023.
    [bib] [data] [abstract]

  • Evaluating Verifiability in Generative Search Engines
    Nelson F. Liu, Tianyi Zhang, and Percy Liang.
    In Findings of ACL: EMNLP, 2023.
    [bib] [code] [abstract]

  • Are Sample-Efficient NLP Models More Robust?
    Nelson F. Liu, Ananya Kumar, Percy Liang, and Robin Jia.
    In Annual Meeting of the Association for Computational Linguistics (ACL), 2023.
    [bib] [abstract]

  • Do Question Answering Modeling Improvements Hold Across Benchmarks?
    Nelson F. Liu, Tony Lee, Robin Jia, and Percy Liang.
    In Annual Meeting of the Association for Computational Linguistics (ACL), 2023.
    [bib] [abstract]

  • Dyna-bAbI: unlocking bAbI's potential with dynamic synthetic benchmarking
    Ronen Tamari, Kyle Richardson, Noam Kahlon, Aviad Sar-Shalom, Nelson F. Liu, Reut Tsarfaty, and Dafna Shahaf.
    In Joint Conference on Lexical and Computational Semantics (*SEM), 2022.
    [bib] [abstract]

  • Identifying the Limits of Cross-Domain Knowledge Transfer for Pretrained Models
    Zhengxuan Wu, Nelson F. Liu, and Christopher Potts.
    In ACL Workshop on Representation Learning for NLP (RepL4NLP), 2022. (Best Paper Award).
    [bib] [abstract] [code]

  • Making Heads and Tails of Models with Marginal Calibration for Sparse Tagsets
    Michael Kranzlein, Nelson F. Liu, and Nathan Schneider.
    In Findings of ACL: EMNLP, 2021.
    [bib] [abstract]

  • Lexical Semantic Recognition
    Nelson F. Liu, Daniel Hershcovich, Michael Kranzlein, and Nathan Schneider.
    In ACL Workshop on Multiword Expressions (MWE), 2021.
    [bib] [abstract] [code]

  • Evaluating Models' Local Decision Boundaries via Contrast Sets
    Matt Gardner, Yoav Artzi, Victoria Basmova, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hannaneh Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, Ally Zhang and Ben Zhou.
    In Findings of ACL: EMNLP, 2020.
    [bib] [abstract] [dataset]

  • Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning
    Pradeep Dasigi, Nelson F. Liu, Ana Marasović, Noah A. Smith, and Matt Gardner.
    In Conference on Empirical Methods in Natural Language Processing & International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2019.
    [bib] [abstract] [poster] [dataset] [leaderboard]

  • Barack's Wife Hillary: Using Knowledge Graphs for Fact-Aware Language Modeling
    Robert L. Logan IV, Nelson F. Liu, Matthew E. Peters, Matt Gardner, and Sameer Singh.
    In Annual Meeting of the Association for Computational Linguistics (ACL), 2019.
    [bib] [abstract] [poster] [code] [dataset]

  • Linguistic Knowledge and Transferability of Contextual Representations
    Nelson F. Liu, Matt Gardner, Yonatan Belinkov, Matthew E. Peters, and Noah A. Smith.
    In North American Chapter of the Association for Computational Linguistics (NAACL), 2019.
    [bib] [abstract] [slides: pdf, pdf with notes, key] [code]

  • Inoculation by Fine-Tuning: A Method for Analyzing Challenge Datasets
    Nelson F. Liu, Roy Schwartz, and Noah A. Smith.
    In North American Chapter of the Association for Computational Linguistics (NAACL), 2019.
    [bib] [abstract] [slides: pdf, pdf with notes, key] [code]

  • LSTMs Exploit Linguistic Attributes of Data
    Nelson F. Liu, Omer Levy, Roy Schwartz, Chenhao Tan, and Noah A. Smith.
    In ACL Workshop on Representation Learning for NLP (RepL4NLP), 2018. (Best Paper Award).
    [bib] [abstract] [(short) slides] [poster] [code]

  • AllenNLP: A Deep Semantic Natural Language Processing Platform
    Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson F. Liu, Matthew Peters, Michael Schmitz, and Luke Zettlemoyer.
    In ACL Workshop for Natural Language Processing Open Source Software (NLP-OSS), 2018.
    [bib] [abstract] [code]

  • Discovering Phonesthemes with Sparse Regularization
    Nelson F. Liu, Gina-Anne Levow, and Noah A. Smith.
    In NAACL Workshop on Subword and Character Level Models in NLP (SCLeM), 2018.
    [bib] [abstract] [poster] [code]

  • Crowdsourcing Multiple Choice Science Questions
    Johannes Welbl, Nelson F. Liu, and Matt Gardner.
    In EMNLP Workshop on Noisy User-generated Text (W-NUT), 2017.
    [bib] [abstract] [data] [poster]

Miscellany