🧑‍🔬 Artidoro Pagnoni

🏛️ Affiliations

🎓University of WashingtonSeattle · United States2022–2026 · 8 paperscurrent🎓Carnegie Mellon UniversityPittsburgh · United States2020–2022 · 7 papers🎓Harvard UniversityCambridge · United States2018–2019 · 2 papers🏢MicrosoftRedmond · United States2018–2019 · 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.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. QLoRA: Efficient Finetuning of Quantized LLMs (2023 · 2,929 citations)
   Fine-tuning large language models requires a great deal of GPU memory. For example, LLaMA 65B in standard 16-bit mode needs more than 780 GB. The authors propose QLoRA, a method in which the base model is frozen and stored in 4-bit form, and only small LoRA adapters added to every layer of the network are trained.…
2. Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics (2021 · 420 citations)
   Neural abstractive summarizers write fluent text that often misstates facts from the source. Existing factuality metrics could not be compared because there was no shared benchmark, and they all reduce factuality to a yes/no label for a whole summary.

All works

6 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. QLoRA: Efficient Finetuning of Quantized LLMsConference on Neural Information Processing Systems (NeurIPS)Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, et al.2023 · 2,929 citations
2. Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality MetricsConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Artidoro Pagnoni, Vidhisha Balachandran, Yulia Tsvetkov2021 · 420 citations
3. Byte Latent Transformer: Patches Scale Better Than Tokens ↗Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetArtidoro Pagnoni et al.2025 · 73 citations
4. Fast Byte Latent Transformer ↗arXiv (Cornell University) · no summary yetJulie Kallini et al.2026 · 1 citation
5. Compute Optimal Tokenization ↗arXiv (Cornell University) · no summary yetTomasz Limisiewicz et al.2026 · 1 citation
6. Breaking the Token Ceiling: Distilling Smaller, Stronger Byte Models ↗arXiv (Cornell University) · no summary yetKalyani Marathe et al.2026

🤝 Co-authors here

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

• Ari Holtzman
• Luke Zettlemoyer
• Tim Dettmers
• Vidhisha Balachandran
• Yulia Tsvetkov

Venues

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

• Conference of the North American Chapter of the Association for Computational Linguistics (NAACL): NAACL 2021
• Conference on Neural Information Processing Systems (NeurIPS): NeurIPS 2023
• Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

🔗 Cited by

Papers of our library cite their works 1,789 times (of the 149,439 papers whose bibliographies we read); 257 citing papers with a page here →.

Updated 30 October 2026

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