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cs.CL2026
Break Through the Compression Bottleneck: From Theory to Practice
Xiusheng Huang, Lu Wang, Yequan Wang +2
As the parameter size of language models continues to grow, effective model compression is required to reduce their computational and memory overhead. Existing compression methods…
cs.CL2024
Scalable Fine-tuning from Multiple Data Sources: A First-Order Approximation Approach
Dongyue Li, Ziniu Zhang, Lu Wang +1
We study the problem of fine-tuning a language model (LM) for a target task by optimally using the information from auxiliary tasks. This problem has broad applications in NLP,…