1 citations · 1 across the 3 of their papers we have counts for
4 papers
Revisiting On-policy Adversarial Black-Box Distillation: Calibrating Groupwise Reward Geometry for Effective Advantage Construction
Xiao Cui, Mo Zhu, Yulei Qin +3
Black-box distillation is a practical route for transferring capabilities from API-accessible large language models that expose only text outputs into smaller student models. Recen…
Scaling Laws for Speculative Decoding
Siyuan Yan, Mo Zhu, Guo-qing Jiang +8
The escalating demand for efficient decoding in large language models (LLMs) is particularly critical for reasoning-intensive architectures like OpenAI-o3 and DeepSeek-R1, which de…
Multi-Level Optimal Transport for Universal Cross-Tokenizer Knowledge Distillation on Language Models
Xiao Cui, Mo Zhu, Yulei Qin +3
Knowledge distillation (KD) has become a prevalent technique for compressing large language models (LLMs). Existing KD methods are constrained by the need for identical tokenizers…
Code-switching Language Modeling With Bilingual Word Embeddings: A Case Study for Egyptian Arabic-English
Injy Hamed, Moritz Zhu, Mohamed Elmahdy +2
Code-switching (CS) is a widespread phenomenon among bilingual and multilingual societies. The lack of CS resources hinders the performance of many NLP tasks. In this work, we expl…