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Paul Kishan Rubenstein

Paul Kishan Rubenstein has 2 papers in our library, in 2023, most of them on Language models & pretraining, Multilingual & cross-lingual NLP; often with Wei Han, Ankur Bapna, Chung-Cheng Chiu.

2 papers in our library · 2023

OpenAlex · h-index 10 · 21 works · 1,580 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 4,747 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #6197

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

🔬Max Planck Institute for Intelligent SystemsStuttgart · Germany2017–2019 · 6 papers🔬Max Planck SocietyMunich · Germany2017–2019 · 6 papers🎓University of CambridgeCambridge · United Kingdom2016–2019 · 6 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 6 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 worksSignal Processing · 2 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksMultilingual & cross-lingual NLP · 1 work

 Works by year

2020s

20232 papers

 Papers

The most cited first (OpenAlex and our library).

  1. AudioPaLM: A Large Language Model That Can Speak and Listen (2023 · 171 citations)
    AudioPaLM, from Google, tackles a split in current systems. Text LLMs hold broad linguistic knowledge but cannot hear or produce speech. Audio language models capture voice and prosody but lack that knowledge. The authors start from a text-only PaLM-2 (or PaLM) checkpoint and enlarge its embedding table with discrete…
  2. SLM: Bridge the thin gap between speech and text foundation models (2023 · 54 citations)
    Building speech-language systems usually demands large amounts of paired audio-text data or costly end-to-end training.

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. Gemma 3 Technical Report ↗arXiv:2503.19786 · no summary yetGemma Team et al.2025 · 1,147 citations
  2. AudioPaLM: A Large Language Model That Can Speak and ListenarXiv:2306.12925Paul K. Rubenstein, Chulayuth Asawaroengchai, Duc Dung Nguyen, et al.2023 · 171 citations
  3. SLM: Bridge the thin gap between speech and text foundation modelsIEEE Automatic Speech Recognition and Understanding Workshop (ASRU)Mingqiu Wang, Wei Han, Izhak Shafran, et al.2023 · 54 citations
  4. Learning Translation Quality Evaluation on Low Resource Languages from Large Language Models ↗arXiv:2302.03491 · no summary yetAmirkeivan Mohtashami, Mauro Verzetti, Paul K. Rubenstein2023 · 3 citations

 Co-authors here

9 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 4,747 times (of the 149,439 papers whose bibliographies we read); 1,116 citing papers with a page here →.

Updated

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