Zhengao Li — accepted AI-conference papers

2026

Query-Efficient Domain Knowledge Stealing Against Large Language Models

Zhengao Li, Xiaopeng Yuan, Bolin Shen, Kien Le, Haohan Wang, Xugui Zhou +2

AAAI 2026technical

Large language models (LLMs) concentrate substantial knowledge in specialized domains due to extensive pretraining and instruction tuning, and they are now central to commercial and scientific practice. Yet access is usually limited to costly, rate-limited interfaces, which motivates methods that ca

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2026

ToMoE: Converting Dense Large Language Models to Mixture-of-Experts through Dynamic Structural Pruning

Shangqian Gao, Ting Hua, Reza Shirkavand, Chi-Heng Lin, Zheng Tang, Zhengao Li +7

ICML 2026poster

Large Language Models (LLMs) demonstrate remarkable capabilities but face deployment challenges due to their high computational demands. Traditional pruning methods reduce these costs by permanently removing parameters, which inevitably leads to performance degradation. To mitigate this issue, we pr…

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