3 citations · 3 across the 16 of their papers we have counts for
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MoRe Fine-Tuning with 10x Fewer Parameters
Wenxuan Tan, Nicholas Roberts, Tzu-Heng Huang +5
Parameter-efficient fine-tuning (PEFT) techniques have unlocked the potential to cheaply and easily specialize large pretrained models. However, the most prominent approaches, like…
The ALCHEmist: Automated Labeling 500x CHEaper Than LLM Data Annotators
Tzu-Heng Huang, Catherine Cao, Vaishnavi Bhargava +1
Large pretrained models can be used as annotators, helping replace or augment crowdworkers and enabling distilling generalist models into smaller specialist models. Unfortunately,…
Pretrained Hybrids with MAD Skills
Nicholas Roberts, Samuel Guo, Zhiqi Gao +5
While Transformers underpin modern large language models (LMs), there is a growing list of alternative architectures with new capabilities, promises, and tradeoffs. This makes choo…