2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.CL2024
M2QA: Multi-domain Multilingual Question Answering
Leon Engländer, Hannah Sterz, Clifton Poth +3
Generalization and robustness to input variation are core desiderata of machine learning research. Language varies along several axes, most importantly, language instance (e.g. Fre…
cs.CL2024★ 1 cited
Scaling Sparse Fine-Tuning to Large Language Models
Alan Ansell, Ivan Vulić, Hannah Sterz +2
Large Language Models (LLMs) are difficult to fully fine-tune (e.g., with instructions or human feedback) due to their sheer number of parameters. A family of parameter-efficient s…
cs.CL2023★ 2 cited
Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning
Clifton Poth, Hannah Sterz, Indraneil Paul +7
We introduce Adapters, an open-source library that unifies parameter-efficient and modular transfer learning in large language models. By integrating 10 diverse adapter methods int…