
Selected Papers
(* equal contribution)
​Hi! I’m Yanhong Li. I am currently a pre-doctoral researcher at the Allen Institute for AI (AI2), advised by Luca Soldaini. I am broadly interested in improving the efficiency of language modeling, including data efficiency, efficient model architectures, and inference methods. I also love exploring generative models through their representation space (I especially love embeddings). I’m always happy to chat about research (yanhongl@allenai.org)! :)
This past summer, I was a visiting student at MIT CSAIL, supervised by Prof. Yoon Kim and mentored by the wonderful Songlin Yang (I distilled so much from her!!).
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I graduated from the University of Chicago in 2025. During undergrad, I was extremely grateful to work with Prof. David McAllester (TTIC), Prof. Karen Livescu (TTIC), Prof. Michael Maire (UChicago), Prof. Jiawei Zhou (Stony Brook), and Prof. Allyson Ettinger (AI2). I couldn’t appreciate their invaluable insights and support more. A huge thank-you as well to my early mentors: David Yunis (TTIC), Chenghao Yang (UChicago), and Marcelo Sandoval-Castañeda (TTIC)—they taught me so much when I knew nothing about research. Be sure to check out their interesting work!
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Yanhong Li*, Zixuan Lan*, Jiawei Zhou, Text or Pixels? Evaluating Efficiency and Understanding of LLMs with Visual Text Inputs. EMNLP 2025 Findings.
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Yanhong Li, Karen Livescu, Jiawei Zhou. Chunk-Distilled Language Modeling. ICLR 2025.
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Yanhong Li, David Yunis, David McAllester, Jiawei Zhou. Context-Efficient Retrieval with Factual Decomposition. NAACL 2025 Main Conference.
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Ming Li, Yanhong Li, Tianyi Zhou. What Happened in LLMs Layers when Trained for Fast vs. Slow Thinking: A Gradient Perspective. ACL 2025 Main Conference.
Yanhong Li*, Chenghao Yang*, Allyson Ettinger. When Hindsight is Not 20/20: Testing Limits on Reflective Thinking in Large Language Models. NAACL 2024 Findings.​
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For the full list, please see my Google Scholar page. ​