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Simon Baumgartner

Simon Baumgartner has 3 papers in our library, in 2021–2025, most of them on Information retrieval & search, Tokenization & segmentation; often with Zhen Qin, Jiaming Shen, Tianqi Liu.

3 papers in our library · 2021–2025

OpenAlex · ORCID · Wikidata · h-index 6 · 16 works · 268 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 175 times in our library · ⭐ Paper Stars ranking: #5494

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

🏢GoogleMountain View · United States2018–2025 · 11 paperscurrent🏢KaggleSan Francisco2025 · 1 papercurrent
🏛️Google LLCNew York · United States2014Employment since 2014 · Research

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

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

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 1 workInformation Systems · 1 work

🧭 Topics 2Research subjects and languages the work is about.Tokenization & segmentation · 1 workInformation retrieval & search · 1 work

💡 Concepts 2Key ideas and methods explained on our concept pages.Subword tokenization · 1 workTokenization · 1 work

 Works by year

2020s

20251 paper20241 paper20211 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Charformer: Fast Character Transformers via Gradient-based Subword Tokenization (2021 · 186 citations)
    Most NLP models rely on a subword tokenizer that is fixed before training. This makes them fragile to rare words, typos and noisy text. It also breaks low-resource languages into many fragments and makes adaptation to new domains harder. Feeding raw characters to a Transformer avoids these issues, but the long…
  2. LiPO: Listwise Preference Optimization through Learning-to-Rank (2025 · 31 citations)
    Most alignment methods that skip full RLHF, such as DPO and SLiC, learn only from pairs of responses. Human or reward-model feedback, however, often ranks several responses to the same prompt. The authors treat alignment as a Learning-to-Rank (LTR) problem. The prompt plays the role of a query, the sampled responses…
  3. Boosting Reward Model with Preference-Conditional Multi-Aspect Synthetic Data Generation (2024 · 5 citations)
    Reward models used to align LLMs need preference data: a prompt, two candidate answers and a label saying which is better.

 Co-authors here

12 of 22 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 175 times (of the 149,439 papers whose bibliographies we read); 44 citing papers with a page here →.

Updated

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