🧑‍🔬 Lili Yu

🏛️ Affiliations

🎓China University of GeosciencesWuhan · China2024–2025 · 3 paperscurrent🎓University of ChicagoChicago · United States2025 · 1 papercurrent🏥ShenZhen People’s HospitalShenzhen · China2020–2024 · 5 papers🎓Shijiazhuang Information Engineering Vocational CollegeShijiazhuang · China2013–2016 · 5 papers🔬Lawrence Berkeley National LaboratoryBerkeley · United States2022–2023 · 3 papers🎓Jiamusi UniversityJiamusi · China2011–2020 · 3 papers🎓University of WashingtonSeattle · United States2023–2024 · 2 papers🏛️Chinese Academy of SciencesBeijing · China2010–2021 · 2 papers🎓Fujian Normal UniversityFuzhou · China2010–2011 · 2 papers🎓Nanjing Normal UniversityNanjing · China2009 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 13 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. Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model (2024 · 83 citations)
   Generative models usually handle text with next-token prediction and images with diffusion. Combining the two typically means either attaching a separate diffusion model to a language model or quantizing images into discrete tokens, which loses information. Transfusion trains a single transformer on mixed text–image…
2. Scaling Laws for Generative Mixed-Modal Language Models (2023 · 45 citations)
   Single generative models can treat images, speech, code and text as token streams. It is unclear, however, whether added scale overcomes the interference that arises when several modalities share one network. The authors ran more than 250 training experiments covering seven modalities: Text, Image, Image-Text…

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. Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal ModelarXiv:2408.11039Chunting Zhou, Lili Yu, Arun Babu, et al.2024 · 83 citations
2. 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
3. Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning ↗arXiv (Cornell University) · no summary yetLili Yu et al.2023 · 58 citations
4. Scaling Laws for Generative Mixed-Modal Language ModelsarXiv:2301.03728Armen Aghajanyan, Lili Yu, Alexis Conneau, et al.2023 · 45 citations

🤝 Co-authors here

12 of 15 co-authors with a page, the most papers together first.

• Luke Zettlemoyer ×2
• Omer Levy ×2
• Alexis Conneau
• Armen Aghajanyan
• Arun Babu
• Chunting Zhou
• Jacob Kahn
• Karen Hambardzumyan
• Kushal Tirumala
• Michihiro Yasunaga
• Naman Goyal
• Stephen Roller

Venues

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

• 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 202 times (of the 149,439 papers whose bibliographies we read); 45 citing papers with a page here →.

Updated 2 November 2026

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