18 citations · 18 across the 2 of their papers we have counts for
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cs.CL2025
One Size Does Not Fit All: A Distribution-Aware Sparsification for More Precise Model Merging
Yingfeng Luo, Dingyang Lin, Junxin Wang +8
Model merging has emerged as a compelling data-free paradigm for multi-task learning, enabling the fusion of multiple fine-tuned models into a single, powerful entity. A key techni…
cs.CL2024★ 18 cited
Efficient Prompting Methods for Large Language Models: A Survey
Kaiyan Chang, Songcheng Xu, Chenglong Wang +4
Prompting is a mainstream paradigm for adapting large language models to specific natural language processing tasks without modifying internal parameters. Therefore, detailed suppl…