37 citations · 62 across the 19 of their papers we have counts for
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cs.CL2023
Data Diversity Matters for Robust Instruction Tuning
Alexander Bukharin, Shiyang Li, Zhengyang Wang +6
Recent works have shown that by curating high quality and diverse instruction tuning datasets, we can significantly improve instruction-following capabilities. However, creating su…
cs.CL2023★ 37 cited
AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning
Qingru Zhang, Minshuo Chen, Alexander Bukharin +5
Fine-tuning large pre-trained language models on downstream tasks has become an important paradigm in NLP. However, common practice fine-tunes all of the parameters in a pre-traine…