Rethinking Kullback-Leibler Divergence in Knowledge Distillation for Large Language Models (2025 · 47 citations)
Distilling large language models usually means training a student to match the teacher's next-token distribution with a Kullback-Leibler loss.
Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies (2024 · 46 citations)
Standard scaling laws for LLMs predict loss from parameter count and training data but treat vocabulary size as fixed.
Find Your Optimal Teacher: Personalized Data Synthesis via Router-Guided Multi-Teacher Distillation (2026 · 13 citations)
Small LLMs are usually distilled by fine-tuning them on outputs from the most powerful available teacher. Prior work shows this can backfire: very strong teachers may produce answers that are too complex or too far from what the student can absorb. Earlier remedies (Mix, CAR) still make every candidate teacher answer…

All works

7 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

This Looks Like That: Deep Learning for Interpretable Image Recognition ↗arXiv (Cornell University) · no summary yetChaofan Chen et al.2018 · 590 citations
A case-based interpretable deep learning model for classification of mass lesions in digital mammography ↗Nature Machine Intelligence · no summary yetAlina Jade Barnett et al.2021 · 164 citations
Rethinking Kullback-Leibler Divergence in Knowledge Distillation for Large Language ModelsInternational Conference on Computational Linguistics (COLING)Taiqiang Wu, Chaofan Tao, Jiahao Wang, et al.2025 · 47 citations
Scaling Laws with Vocabulary: Larger Models Deserve Larger VocabulariesarXiv:2407.13623Chaofan Tao, Qian Liu, Longxu Dou, et al.2024 · 46 citations
IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography ↗arXiv (Cornell University) · no summary yetAlina Jade Barnett et al.2021 · 15 citations
Find Your Optimal Teacher: Personalized Data Synthesis via Router-Guided Multi-Teacher DistillationAnnual Meeting of the Association for Computational Linguistics (ACL)Hengyuan Zhang, Shiping Yang, Xiao Liang, et al.2026 · 13 citations
Interpretable Mammographic Image Classification using Case-Based Reasoning and Deep Learning ↗arXiv (Cornell University) · no summary yetAlina Jade Barnett et al.2021 · 7 citations

Co-authors here

12 of 18 co-authors with a page, the most papers together first.

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

Papers of our library cite their works 160 times (of the 149,439 papers whose bibliographies we read); 32 citing papers with a page here →.

Updated 30 October 2026