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Shawn Tan

Shawn Tan has 2 papers in our library, in 2019–2023, most of them on Reasoning; often with Yikang Shen, Aaron Courville, Chuang Gan.

2 papers in our library · 2019–2023

OpenAlex · ORCID · h-index 15 · 56 works · 734 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 21 times in our library · ⭐ Paper Stars ranking: #8594

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Université de MontréalMontreal · Canada2017–2023 · 16 papers🔬Mila - Quebec Artificial Intelligence InstituteMontreal · Canada2020–2023 · 10 papers🏢IBMArmonk · United States2024–2026 · 7 papers🎓National University of SingaporeSingapore · Singapore2003–2018 · 6 papers🎓Massachusetts Institute of TechnologyCambridge · United States2025–2026 · 2 papers🎓Centre Universitaire de MilaMila · Algeria2020–2023 · 2 papers🔬Canadian Institute for Advanced ResearchToronto · Canada2020 · 2 papers🎓McGill UniversityMontreal · Canada2020 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 1Research subjects and languages the work is about.Reasoning · 1 work

 Works by year

2020s

20231 paper

2010s

20191 paper

 Papers

The most cited first (OpenAlex and our library).

  1. ModuleFormer: Modularity Emerges from Mixture-of-Experts (2023 · 21 citations)
    Dense large language models use every parameter for every token. This makes them costly to run and hard to update with new knowledge without catastrophic forgetting. Earlier modular approaches based on Sparse Mixture of Experts, such as DEMix, depended on domain-labeled training data. The authors propose…
  2. Investigating Biases in Textual Entailment Datasets (2019 · 7 citations)
    The paper asks whether crowdsourced entailment corpora carry annotation artifacts similar to those found earlier in VQA, and whether NLI models exploit them.

 Co-authors here

5 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 21 times (of the 149,439 papers whose bibliographies we read); 2 citing papers with a page here →.

Updated

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