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cs.CL2026
Compressed Sensing for Capability Localization in Large Language Models
Anna Bair, Yixuan Even Xu, Mingjie Sun +1
Large language models (LLMs) exhibit a wide range of capabilities, including mathematical reasoning, code generation, and linguistic behaviors. We show that Transformer architectur…
cs.CL2024
Bi-Mamba: Towards Accurate 1-Bit State Space Models
Shengkun Tang, Liqun Ma, Haonan Li +2
The typical Selective State-Space Model (SSM) used in Mamba addresses several limitations of Transformers, such as the quadratic computational complexity with respect to sequence l…
cs.CL2024★ 1 cited
FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive Distillation
Liqun Ma, Mingjie Sun, Zhiqiang Shen
This work presents a Fully BInarized Large Language Model (FBI-LLM), demonstrating for the first time how to train a large-scale binary language model from scratch (not the partial…