Portrait de Hang Zhang

Hang Zhang

hang.zhang@mila.quebec
Doctorat - McGill
Superviseur⋅e principal⋅e
Warren Gross
Sujets de recherche
Compression de modèles
Grands modèles de langage (LLM)
Traitement du langage naturel
Google Scholar
GitHub

Publications

PPL-Factory: Task-Aware and Budget-Aware Data Selection from Language Modeling to Reasoning
Hang Zhang
Warren J. Gross
Not all training samples contribute equally to large language model fine-tuning. Selecting informative training samples can reduce the compu… (voir plus)tational cost while preserving downstream performance. Many existing data selection methods rely on indirect heuristics, such as data quality, diversity or reasoning trace length. However, the effectiveness of these fixed criteria is task-dependent and difficult to generalize across diverse downstream tasks. Perplexity-based data selection provides a simple and model-aware solution to estimate the sample difficulty, but existing approaches typically score the entire training sequence and ignore the difference in learning objectives of language modeling and reasoning tasks. In this paper, we propose PPL-Factory, a simple and interpretable data selection framework that combines task-aware perplexity-based scores and data budget-aware selection criteria. Experiments on GSM8K demonstrate that PPL-Factory outperforms other state-of-the-art data selection methods using only
2026-07-19
arXiv (prépublication)
doi.org
arxiv.org
Efficient Two-Stage Progressive Quantization of BERT
Phuoc-Hoan Charles Le
Arash Ardakani
Amir Ardakani
Hang Zhang
Yuyan Chen
James J. Clark
Brett H. Meyer
Warren J. Gross
Charles Le, Arash Ardakani, Amir Ardakani, Hang Zhang, Yuyan Chen, James Clark, Brett Meyer, Warren Gross. Proceedings of the Third Workshop… (voir plus) on Simple and Efficient Natural Language Processing (SustaiNLP). 2022.
2021-12-31
SustaiNLP @ Conference on Empirical Methods in Natural Language Processing (publié)
doi.org