---
_id: '18117'
abstract:
- lang: eng
  text: "We investigate parameter-efficient fine-tuning (PEFT) methods that can provide
    good accuracy under limited computational and memory budgets in the context of
    large language models (LLMs). We present a new PEFT method called Robust Adaptation
    (RoSA) inspired by robust principal component analysis that jointly trains low-rank\r\n
    and highly-sparse components on top of a set of fixed pretrained weights to efficiently
    approximate the performance of a full-fine-tuning (FFT) solution. Across a series
    of challenging generative tasks such as grade-school math and SQL query generation,
    which require fine-tuning for good performance, we show that RoSA outperforms
    LoRA, pure sparse fine-tuning, and alternative hybrid methods at the same parameter
    budget, and can even recover the performance of FFT on some tasks. We provide
    system support for RoSA to complement the training algorithm, specifically in
    the form of sparse GPU kernels which enable memory- and computationally-efficient
    training, and show that it is also compatible with low-precision base weights,
    resulting in the first joint representation combining quantization, low-rank and
    sparse approximations. Our code is available at https://github.com/IST-DASLab/RoSA."
acknowledgement: The authors would like to thank Eldar Kurtic for experimental support
  and useful suggestions throughout the project
article_processing_charge: No
arxiv: 1
author:
- first_name: Mahdi
  full_name: Nikdan, Mahdi
  id: 66374281-f394-11eb-9cf6-869147deecc0
  last_name: Nikdan
- first_name: Soroush
  full_name: Tabesh, Soroush
  id: 06000900-6068-11ef-8d61-c2472ef2e752
  last_name: Tabesh
  orcid: 0009-0003-4119-6281
- first_name: Elvir
  full_name: Crncevic, Elvir
  id: 41888001-440d-11ef-8299-d0e838b8185e
  last_name: Crncevic
- first_name: Dan-Adrian
  full_name: Alistarh, Dan-Adrian
  id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
  last_name: Alistarh
  orcid: 0000-0003-3650-940X
citation:
  ama: 'Nikdan M, Tabesh S, Crncevic E, Alistarh D-A. RoSA: Accurate parameter-efficient
    fine-tuning via robust adaptation. In: <i>Proceedings of the 41st International
    Conference on Machine Learning</i>. Vol 235. ML Research Press; 2024:38187-38206.'
  apa: 'Nikdan, M., Tabesh, S., Crncevic, E., &#38; Alistarh, D.-A. (2024). RoSA:
    Accurate parameter-efficient fine-tuning via robust adaptation. In <i>Proceedings
    of the 41st International Conference on Machine Learning</i> (Vol. 235, pp. 38187–38206).
    Vienna, Austria: ML Research Press.'
  chicago: 'Nikdan, Mahdi, Soroush Tabesh, Elvir Crncevic, and Dan-Adrian Alistarh.
    “RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation.” In <i>Proceedings
    of the 41st International Conference on Machine Learning</i>, 235:38187–206. ML
    Research Press, 2024.'
  ieee: 'M. Nikdan, S. Tabesh, E. Crncevic, and D.-A. Alistarh, “RoSA: Accurate parameter-efficient
    fine-tuning via robust adaptation,” in <i>Proceedings of the 41st International
    Conference on Machine Learning</i>, Vienna, Austria, 2024, vol. 235, pp. 38187–38206.'
  ista: 'Nikdan M, Tabesh S, Crncevic E, Alistarh D-A. 2024. RoSA: Accurate parameter-efficient
    fine-tuning via robust adaptation. Proceedings of the 41st International Conference
    on Machine Learning. ICML: International Conference on Machine Learning vol. 235,
    38187–38206.'
  mla: 'Nikdan, Mahdi, et al. “RoSA: Accurate Parameter-Efficient Fine-Tuning via
    Robust Adaptation.” <i>Proceedings of the 41st International Conference on Machine
    Learning</i>, vol. 235, ML Research Press, 2024, pp. 38187–206.'
  short: M. Nikdan, S. Tabesh, E. Crncevic, D.-A. Alistarh, in:, Proceedings of the
    41st International Conference on Machine Learning, ML Research Press, 2024, pp.
    38187–38206.
conference:
  end_date: 2024-07-27
  location: Vienna, Austria
  name: 'ICML: International Conference on Machine Learning'
  start_date: 2024-07-21
corr_author: '1'
date_created: 2024-09-22T22:01:44Z
date_published: 2024-09-01T00:00:00Z
date_updated: 2024-10-01T08:22:01Z
day: '01'
department:
- _id: DaAl
- _id: GradSch
external_id:
  arxiv:
  - '2401.04679'
intvolume: '       235'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://doi.org/10.48550/arXiv.2401.04679
month: '09'
oa: 1
oa_version: Preprint
page: 38187-38206
publication: Proceedings of the 41st International Conference on Machine Learning
publication_identifier:
  eissn:
  - 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
related_material:
  link:
  - relation: software
    url: https://github.com/IST-DASLab/RoSA
scopus_import: '1'
status: public
title: 'RoSA: Accurate parameter-efficient fine-tuning via robust adaptation'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 235
year: '2024'
...