@inproceedings{ni-etal-2026-lora,
title = "Is {L}o{RA} Robust to Adversarial Attacks? Expectation-Based Regularization for Parameter-Efficient Adversarial Training",
author = "Ni, Mingze and
Gong, Yongshun and
Yang, Xinghao and
Liu, Wei",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1638/",
doi = "10.18653/2026.findings-emnlp.1638",
pages = "34848--34861",
ISBN = "979-8-89176-462-0",
abstract = "Low-Rank Adaptation (LoRA) is widely used for parameter-efficient fine-tuning, yet its robustness to textual adversarial perturbations remains understudied. Existing adversarial training methods often improve robustness by solving costly min-max objectives with inner perturbation optimization with suboptimal results. In this research, we propose Expectation-Regularized LoRA (ER-LoRA), a simple expectation-based regularization approach that improves the robustness of LoRA without requiring inner maximization or adversarial example generation. ER-LoRA smooths predictions over a LoRA-aware low-dimensional perturbation distribution and admits a closed-form curvature-aware objective via a local Gaussian approximation. It adds no trainable parameters and keeps the LoRA architecture unchanged. Experiments on adversarial NLP benchmarks and textual attacks show that ER-LoRA improves robustness while preserving clean accuracy and parameter efficiency."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ni-etal-2026-lora">
<titleInfo>
<title>Is LoRA Robust to Adversarial Attacks? Expectation-Based Regularization for Parameter-Efficient Adversarial Training</title>
</titleInfo>
<name type="personal">
<namePart type="given">Mingze</namePart>
<namePart type="family">Ni</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yongshun</namePart>
<namePart type="family">Gong</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xinghao</namePart>
<namePart type="family">Yang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Wei</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: EMNLP 2026</title>
</titleInfo>
<name type="personal">
<namePart type="given">Hung-Yi</namePart>
<namePart type="family">Lee</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sunipa</namePart>
<namePart type="family">Dev</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jessy</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Desmond</namePart>
<namePart type="family">Elliott</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Budapest, Hungary</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-462-0</identifier>
</relatedItem>
<abstract>Low-Rank Adaptation (LoRA) is widely used for parameter-efficient fine-tuning, yet its robustness to textual adversarial perturbations remains understudied. Existing adversarial training methods often improve robustness by solving costly min-max objectives with inner perturbation optimization with suboptimal results. In this research, we propose Expectation-Regularized LoRA (ER-LoRA), a simple expectation-based regularization approach that improves the robustness of LoRA without requiring inner maximization or adversarial example generation. ER-LoRA smooths predictions over a LoRA-aware low-dimensional perturbation distribution and admits a closed-form curvature-aware objective via a local Gaussian approximation. It adds no trainable parameters and keeps the LoRA architecture unchanged. Experiments on adversarial NLP benchmarks and textual attacks show that ER-LoRA improves robustness while preserving clean accuracy and parameter efficiency.</abstract>
<identifier type="citekey">ni-etal-2026-lora</identifier>
<identifier type="doi">10.18653/2026.findings-emnlp.1638</identifier>
<location>
<url>https://aclanthology.org/2026.findings-emnlp.1638/</url>
</location>
<part>
<date>2026-10</date>
<extent unit="page">
<start>34848</start>
<end>34861</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Is LoRA Robust to Adversarial Attacks? Expectation-Based Regularization for Parameter-Efficient Adversarial Training
%A Ni, Mingze
%A Gong, Yongshun
%A Yang, Xinghao
%A Liu, Wei
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F ni-etal-2026-lora
%X Low-Rank Adaptation (LoRA) is widely used for parameter-efficient fine-tuning, yet its robustness to textual adversarial perturbations remains understudied. Existing adversarial training methods often improve robustness by solving costly min-max objectives with inner perturbation optimization with suboptimal results. In this research, we propose Expectation-Regularized LoRA (ER-LoRA), a simple expectation-based regularization approach that improves the robustness of LoRA without requiring inner maximization or adversarial example generation. ER-LoRA smooths predictions over a LoRA-aware low-dimensional perturbation distribution and admits a closed-form curvature-aware objective via a local Gaussian approximation. It adds no trainable parameters and keeps the LoRA architecture unchanged. Experiments on adversarial NLP benchmarks and textual attacks show that ER-LoRA improves robustness while preserving clean accuracy and parameter efficiency.
%R 10.18653/2026.findings-emnlp.1638
%U https://aclanthology.org/2026.findings-emnlp.1638/
%U https://doi.org/10.18653/2026.findings-emnlp.1638
%P 34848-34861
Markdown (Informal)
[Is LoRA Robust to Adversarial Attacks? Expectation-Based Regularization for Parameter-Efficient Adversarial Training](https://aclanthology.org/2026.findings-emnlp.1638/) (Ni et al., Findings 2026)
ACL