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Max Ryabinin

Max Ryabinin has 2 papers in our library, in 2021–2023, most of them on Language models & pretraining, Multilingual & cross-lingual NLP, Reasoning; often with Beidi Chen, Christopher Re, Clark Barrett.

2 papers in our library · 2021–2023

OpenAlex · h-index 10 · 44 works · 1,177 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 624 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #3483

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

🎓National Research University Higher School of EconomicsMoscow · Russia2020–2024 · 10 papers🏢YandexMoscow · Russia2020–2023 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 more institutions on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksHardware and Architecture · 1 workComputer Vision and Pattern Recognition · 1 work

🧭 Topics 5Research subjects and languages the work is about.GPU & parallel computing · 1 workLanguage models & pretraining · 1 workInterpretability · 1 workMultilingual & cross-lingual NLP · 1 workReasoning · 1 work

 Works by year

2020s

20231 paperFlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU20211 paperIt's All in the Heads: Using Attention Heads as a Baseline for Cross-Lingual Transfer in Commonsense Reasoning

 Papers

The most cited first (OpenAlex and our library).

  1. FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU (2023 · 399 citations)
    Running very large language models usually requires several expensive accelerators. GPT-175B alone needs about 325GB just for its weights. Many workloads, however, are batch jobs where latency matters little, such as benchmarking, information extraction and data wrangling. The authors build FlexGen, an inference…
  2. It's All in the Heads: Using Attention Heads as a Baseline for Cross-Lingual Transfer in Commonsense Reasoning (2021 · 69 citations)
    Commonsense reasoning benchmarks such as the Winograd Schema Challenge are mostly in English, and versions in other languages define the task in slightly different ways.

All works

3 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. FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPUarXiv:2303.06865Ying Sheng, Lianmin Zheng, Binhang Yuan, et al.2023 · 399 citations
  2. It's All in the Heads: Using Attention Heads as a Baseline for Cross-Lingual Transfer in Commonsense ReasoningFindings of the Association for Computational LinguisticsAlexey Tikhonov, Max Ryabinin2021 · 69 citations
  3. Refined Assessment for Translation Evaluation: Rethinking Machine Translation Evaluation in the Era of Human-Level SystemsFindings of the Association for Computational Linguistics: EMNLP 2025 · original ↗ · no summary yetDmitry Popov et al.2025 · 2 citations

 Co-authors here

10 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 624 times (of the 141,840 papers whose bibliographies we read); 37 citing papers with a page here →.

Updated

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