@inproceedings{liu-etal-2026-look,
title = "Look Within or Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning",
author = "Liu, YongKang and
Xu, Xingle and
Nie, Ercong and
Wang, Zijing and
Feng, Shi and
Wang, Daling and
Li, Qian and
Schuetze, Hinrich",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.2208/",
pages = "47800--47820",
ISBN = "979-8-89176-390-6",
abstract = "\textbf{P}arameter-\textbf{E}fficient \textbf{F}ine-\textbf{T}uning (\textbf{PEFT}) has become a popular alternative to \textbf{F}ull-Parameter \textbf{F}ine-\textbf{T}uning (\textbf{FFT}), achieving similar performance on many benchmarks with far lower computational and memory costs. Yet, its effectiveness on complex tasks such as reasoning and instruction-following remains unclear. In this work, we provide a theoretical and empirical comparison of PEFT and FFT in terms of representational capacity and robustness. We show that PEFT{'}s solution space is a strict subset of FFT{'}s and derive upper bounds revealing how its restricted parameterization limits expressiveness and increases vulnerability to perturbations. Experiments on 20 datasets and 11 adversarial test sets support these findings, indicating that while PEFT performs well on standard tasks, its weaknesses on complex and adversarial settings call for new directions beyond current PEFT paradigms.The source code is in the anonymous GitHub repository[https://anonymous.4open.science/r/PEFTEval-E2AC ]."
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<abstract>Parameter-Efficient Fine-Tuning (PEFT) has become a popular alternative to Full-Parameter Fine-Tuning (FFT), achieving similar performance on many benchmarks with far lower computational and memory costs. Yet, its effectiveness on complex tasks such as reasoning and instruction-following remains unclear. In this work, we provide a theoretical and empirical comparison of PEFT and FFT in terms of representational capacity and robustness. We show that PEFT’s solution space is a strict subset of FFT’s and derive upper bounds revealing how its restricted parameterization limits expressiveness and increases vulnerability to perturbations. Experiments on 20 datasets and 11 adversarial test sets support these findings, indicating that while PEFT performs well on standard tasks, its weaknesses on complex and adversarial settings call for new directions beyond current PEFT paradigms.The source code is in the anonymous GitHub repository[https://anonymous.4open.science/r/PEFTEval-E2AC ].</abstract>
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%0 Conference Proceedings
%T Look Within or Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning
%A Liu, YongKang
%A Xu, Xingle
%A Nie, Ercong
%A Wang, Zijing
%A Feng, Shi
%A Wang, Daling
%A Li, Qian
%A Schuetze, Hinrich
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F liu-etal-2026-look
%X Parameter-Efficient Fine-Tuning (PEFT) has become a popular alternative to Full-Parameter Fine-Tuning (FFT), achieving similar performance on many benchmarks with far lower computational and memory costs. Yet, its effectiveness on complex tasks such as reasoning and instruction-following remains unclear. In this work, we provide a theoretical and empirical comparison of PEFT and FFT in terms of representational capacity and robustness. We show that PEFT’s solution space is a strict subset of FFT’s and derive upper bounds revealing how its restricted parameterization limits expressiveness and increases vulnerability to perturbations. Experiments on 20 datasets and 11 adversarial test sets support these findings, indicating that while PEFT performs well on standard tasks, its weaknesses on complex and adversarial settings call for new directions beyond current PEFT paradigms.The source code is in the anonymous GitHub repository[https://anonymous.4open.science/r/PEFTEval-E2AC ].
%U https://aclanthology.org/2026.acl-long.2208/
%P 47800-47820
Markdown (Informal)
[Look Within or Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning](https://aclanthology.org/2026.acl-long.2208/) (Liu et al., ACL 2026)
ACL
- YongKang Liu, Xingle Xu, Ercong Nie, Zijing Wang, Shi Feng, Daling Wang, Qian Li, and Hinrich Schuetze. 2026. Look Within or Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 47800–47820, San Diego, California, United States. Association for Computational Linguistics.