ResVaRe: Parameter-efficient fine-tuning for large language models via cross-layer residual vector adaptation and representation editing.
- DOI
- 10.1016/j.neunet.2026.109273
- Published
- 2026 Dec
- Container
- Neural networks : the official journal of the International Neural Network Society
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.neunet.2026.109273,
title = {ResVaRe: Parameter-efficient fine-tuning for large language models via cross-layer residual vector adaptation and representation editing.},
author = {Wang Y and Li Y and Wang Y},
year = {2026},
journal = {Neural networks : the official journal of the International Neural Network Society},
doi = {10.1016/j.neunet.2026.109273},
url = {https://doi.org/10.1016/j.neunet.2026.109273}
}RIS
TY - JOUR TI - ResVaRe: Parameter-efficient fine-tuning for large language models via cross-layer residual vector adaptation and representation editing. AU - Wang Y AU - Li Y AU - Wang Y PY - 2026 JO - Neural networks : the official journal of the International Neural Network Society DO - 10.1016/j.neunet.2026.109273 UR - https://doi.org/10.1016/j.neunet.2026.109273 ER -
APA
Y, W., Y, L., & Y, W. (2026). ResVaRe: Parameter-efficient fine-tuning for large language models via cross-layer residual vector adaptation and representation editing.. Neural networks : the official journal of the International Neural Network Society. https://doi.org/10.1016/j.neunet.2026.109273
Source records
- pubmed · retrieved 2026-09-25T21:49:11.239Z