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Jonathan Pilault

Jonathan Pilault has 2 papers in our library, in 2020–2023, most of them on Language models & pretraining, Summarization; often with Christopher Pal, Orhan Firat, Raymond Li.

2 papers in our library · 2020–2023

OpenAlex · h-index 4 · 21 works · 263 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 145 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #5097

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

🎓Polytechnique MontréalMontreal · Canada2020–2023 · 6 papers🔬Mila - Quebec Artificial Intelligence InstituteMontreal · Canada2020–2023 · 4 papers🏢ZyphraPalo Alto · United States2024 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 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 2Research subjects and languages the work is about.Language models & pretraining · 1 workSummarization · 1 work

 Works by year

2020s

20231 paper20201 paper

 Papers

The most cited first (OpenAlex and our library).

  1. On Extractive and Abstractive Neural Document Summarization with Transformer Language Models (2020 · 261 citations)
    Neural abstractive models struggle with documents several thousand words long. Recurrent seq2seq systems handle long-range dependencies poorly, and transformer language models (TLMs) cannot fit such long inputs in memory. The authors propose a two-stage hybrid. First, an extractive sentence pointer network selects…
  2. Block-State Transformers (2023 · 20 citations)
    Transformers handle language modeling well, but attention cost grows quadratically with input length, and they generalize poorly to long contexts.

All works

4 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. On Extractive and Abstractive Neural Document Summarization with Transformer Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Jonathan Pilault, Raymond Li, Sandeep Subramanian, et al.2020 · 261 citations
  2. Zamba: A Compact 7B SSM Hybrid Model ↗arXiv:2405.16712 · no summary yetPaolo Glorioso et al.2024 · 50 citations
  3. Block-State TransformersarXiv:2306.09539Mahan Fathi, Jonathan Pilault, Orhan Firat, et al.2023 · 20 citations
  4. On Conditional and Compositional Language Model Differentiable Prompting ↗International Joint Conference on Artificial Intelligence · no summary yetJonathan Pilault et al.2023 · 2 citations

 Co-authors here

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

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 145 times (of the 149,439 papers whose bibliographies we read); 25 citing papers with a page here →.

Updated

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