Publications
2024
- In Search of the Long-Tail: Systematic Generation of Long-Tail Inferential Knowledge via Logical Rule Guided Search. [dataset & code]
Huihan Li, Yuting Ning, Zeyi Liao, Siyuan Wang, Xiang Lorraine Li, Ximing Lu, Wenting Zhao, Faeze Brahman, Yejin Choi, Xiang Ren
The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024) Miami, Florida, US. November, 2024.
- Persuasiveness of Generated Free-Text Rationales in Subjective Decisions: A Case Study on Pairwise Argument Ranking. [dataset & code]
Mohamed Elaraby, Diane Litman, Xiang Lorraine Li, Ahmed Magooda
The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024) Findings Miami, Florida, US. November, 2024.
- Every Answer Matters: Evaluating Commonsense with Probabilistic Measures. [dataset & code]
Qi Cheng, Michael Boratko, Pranay Kumar Yelugam, Tim O'Gorman, Nalini Singh, Andrew McCallum, Xiang Lorraine Li
The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) Bangkok, Thailand. August, 2024.
- PlaSma: Making Small Language Models Better Procedural Knowledge Models for (Counterfactual) Planning. [code]
Faeze Brahman, Chandra Bhagavatula, Valentina Pyatkin*, Jena D. Hwang*, Xiang Lorraine Li, Hirona J. Arai, Soumya Sanyal, Keisuke Sakaguchi, Xiang Ren and Yejin Choi (* Equal Contribution)
The Twelfth International Conference on Learning Representations (ICLR 2024) Vienna, Austria. May, 2024.
- UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations. [dataset]
Wenting Zhao, Justin T Chiu, Jena D Hwang, Faeze Brahman, Jack Hessel, Sanjiban Choudhury, Yejin Choi, Xiang Lorraine Li+, Alane Suhr+ (+ Equal Last Author)
2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2024) Mexico City, Mexico. June, 2024.
- Incorporating Geo-Diverse Knowledge into Prompting for Increased Geographical Robustness in Object Recognition. [project page]
Kyle Buettner, Sina Malakouti, Xiang Lorraine Li, Adriana Kovashka
The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 (CVPR 2024) Seattle, US. June, 2024
2023
- Faith and Fate: Limits of Transformers on Compositionality. (Spotlight) [code]
Nouha Dziri*, Ximing Lu*, Melanie Sclar*, Xiang Lorraine Li†, Liwei Jiang†, Bill Yuchen Lin†, Peter West, Chandra Bhagavatula, Ronan Le Bras, Jena D. Hwang, Soumya Sanyal, Sean Welleck, Xiang Ren, Allyson Ettinger, Zaid Harchaoui and Yejin Choi (* Co-first authors; † Co-second authors)
2023 Conference on Neural Information Processing Systems (NeurIPS 2023) New Orleans, US. Dec, 2023.
- Editing Commonsense Knowledge in GPT. [code]
Anshita Gupta*, Debanjan Mondal*, Akshay Krishna Sheshadri*, Wenlong Zhao, Xiang Lorraine Li+, Sarah Wiegreffe+, Niket Tandon+. (* Equal First Author, + Equal Last Author)``
The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023) Singapore. Dec, 2023.
2022
- A Systematic Investigation of Commonsense Knowledge in Large Language Models.
Xiang Lorraine Li, Adhiguna Kuncoro, Jordan Hoffmann, Cyprien de Masson d'Autume, Phil Blunsom, Aida Nematzadeh.
The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP). Abu Dhabi, the United Arab Emirates. December 2022.
- Word2Box: Capturing Set-Theoretic Semantics of Words using Box Embeddings. [code]
Shib Sankar Dasgupta*, Michael Boratko*, Siddhartha Mishra, Shriya Atmakuri, Dhruvesh Patel, Xiang Lorraine Li, Andrew McCallum (* Equal Contribution)
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL). Dublin, Ireland. May, 2022.
2021
- Box-To-Box Transformations for Modeling Joint Hierarchies.
Shib Sankar Dasgupta, Xiang Lorraine Li, Michael Boratko, Dongxu Zhang, Andrew McCallum.
The Sixth Workshop on Representation Learning for NLP at ACL (Rep4NLP@ACL 2021) Virtual. August, 2021.
- Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning. [code]
Xuelu Chen*, Michael Boratko*, Muhao Chen, Shib Sankar Dasgupta, Xiang Lorraine Li, Andrew McCallum. (*Equal Contribution)
2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021) Virtual. June, 2021.
- Looking Beyond Short-Premise Natural Language Inference for Downstream Tasks.
Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar, Xiang Lorraine Li, Pavan Kapanipathi, Kartik Talamadupula.
2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021) Virtual. June, 2021.
- Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval. [code]
Wenhan Xiong*, Xiang Lorraine Li*, Srini Iyer, Jingfei Du, Patrick Lewis, William Yang Wang, Yashar Mehdad, Wen-tau Yih, Sebastian Riedel, Douwe Kiela, Barlas Oğuz. (*Equal Contribution)
Ninth International Conference on Learning Representations (ICLR). Virtual. May, 2021.
2020
- Improving Local Identifiability in Probabilistic Box Embeddings. [code]
Shib Sankar Dasgupta*, Michael Boratko*, Dongxu Zhang, Luke Vilnis, Xiang Lorraine Li , Andrew McCallum. (*Equal Contribution)
Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS). Virtual. Dec, 2020.
- Reading Comprehension as Natural Language Inference: A Semantic Analysis.
Anshuman Mishra*, Dhruvesh Patel*, Aparna Vijayakumar*, Xiang Lorraine Li, Pavan Kapanipathi, Kartik Talamadupula. (*Equal Contribution)
The 9th Joint Conference on Lexical and Computational Semantics (*SEM) Virtual. December, 2020.
- ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning. [data] [project page] [slides]
Michael Boratko*, Xiang Lorraine Li*, Tim O'Gorman*, Rajarshi Das*, Dan Le, Andrew McCallum. (*Equal Contribution)
The 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Virtual. November 2020.
- Representing joint hierarchies with box embeddings. [code] [video]
Dhruvesh Patel*, Shib Sankar Dasgupta*, Michael Boratko, Xiang Li, Luke Vilnis, Andrew McCallum. (*Equal Contribution)
Automated Knowledge Base Construction (AKBC). Virtual. May 2020.
2019
2018
2017
2016
Pre-print
- Scaling Language Models: Methods, Analysis & Insights from Training Gopher.
[pre-print] [DeepMind Blog]
Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, Eliza Rutherford, Tom Hennigan, Jacob Menick, Albin Cassirer, Richard Powell, George van den Driessche, Lisa Anne Hendricks, Maribeth Rauh, Po-Sen Huang, Amelia Glaese, Johannes Welbl, Sumanth Dathathri, Saffron Huang, Jonathan Uesato, John Mellor, Irina Higgins, Antonia Creswell, Nat McAleese, Amy Wu, Erich Elsen, Siddhant Jayakumar, Elena Buchatskaya, David Budden, Esme Sutherland, Karen Simonyan, Michela Paganini, Laurent Sifre, Lena Martens, Xiang Lorraine Li, Adhiguna Kuncoro, Aida Nematzadeh, Elena Gribovskaya, Domenic Donato, Angeliki Lazaridou, Arthur Mensch, Jean-Baptiste Lespiau, Maria Tsimpoukelli, Nikolai Grigorev, Doug Fritz, Thibault Sottiaux, Mantas Pajarskas, Toby Pohlen, Zhitao Gong, Daniel Toyama, Cyprien de Masson d’Autume, Yujia Li, Tayfun Terzi, Vladimir Mikulik, Igor Babuschkin, Aidan Clark, Diego de Las Casas, Aurelia Guy, Chris Jones, James Bradbury, Matthew Johnson, Blake Hechtman, Laura Weidinger, Iason Gabriel, William Isaac, Ed Lockhart, Simon Osindero, Laura Rimell, Chris Dyer, Oriol Vinyals, Kareem Ayoub, Jeff Stanway, Lorrayne Bennett, Demis Hassabis, Koray Kavukcuoglu and Geoffrey Irving
