Zhangming Li — accepted AI-conference papers

2026

MemeBQ:Memory Efficient Binary Quantization of LLMs

Yuanhui Wang, Kunlong Liu, Minnan Pei, Zhangming Li, Peisong Wang, Qinghao Hu

AAAI 2026technical

Recent years have witnessed growing scholarly interest in binary post-training quantization (PTQ) techniques for large language models (LLMs). While state-of-the-art (SOTA) binary quantization methods significantly reduce memory footprint and computational demands, they introduce additional memory o

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2025

LoRaDA: Low-Rank Direct Attention Adaptation for Efficient LLM Fine-tuning

Zhangming Li, Qinghao Hu, Yiqun Chen, Peisong Wang, Yifan Zhang, Jian Cheng

EMNLP 2025

As the parameter size of language models becomes extremely large, fine-tuning them with limited resources has become a challenging task. Latest advancements in parameter-efficient fine-tuning (PEFT) techniques allow for adjustments to only a minor fraction of the parameters of these LLMs. Yet, most

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