@inproceedings{hou-etal-2026-learning,
title = "Learning How to Align: Elastic Domain Adaptation for Improving Reasoning Robustness of Large Language Models",
author = "Hou, Yujie and
Wang, Mei and
Zhong, Yaoyao and
Li, Guozhang and
Zhu, Qiannan and
Huang, Hua",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.392/",
pages = "8995--9018",
ISBN = "979-8-89176-459-0",
abstract = "Despite exceptional performance on static benchmarks, Large Language Models (LLMs) exhibit significant brittleness when evaluated on perturbed variants of the original questions. Current extrinsic interventions, prompt engineering or supervised fine-tuning, overlook an important intrinsic factor underlying this degradation: the distributional shifts in layer activations between original and perturbed questions. To address this, this work formulates the reasoning robustness challenge as an Unsupervised Domain Adaptation (UDA) problem and proposes the Elastic Domain Adaptation (EDA) method. Unlike rigid strategies that enforce a uniform alignment constraint, EDA dynamically aligns layer activations based on the specific layers and perturbation types. Specifically, EDA incorporates an Adaptive Layer Selection (ALS) to identify pivotal layers and employs an Elastic Shift Module (ESM) to selectively pull or push layer representations depending on the perturbation types. Extensive experiments across six LLMs and 15 perturbation types demonstrate that EDA achieves state-of-the-art performance on most perturbed benchmarks while preserving reasoning capability on original benchmarks."
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<abstract>Despite exceptional performance on static benchmarks, Large Language Models (LLMs) exhibit significant brittleness when evaluated on perturbed variants of the original questions. Current extrinsic interventions, prompt engineering or supervised fine-tuning, overlook an important intrinsic factor underlying this degradation: the distributional shifts in layer activations between original and perturbed questions. To address this, this work formulates the reasoning robustness challenge as an Unsupervised Domain Adaptation (UDA) problem and proposes the Elastic Domain Adaptation (EDA) method. Unlike rigid strategies that enforce a uniform alignment constraint, EDA dynamically aligns layer activations based on the specific layers and perturbation types. Specifically, EDA incorporates an Adaptive Layer Selection (ALS) to identify pivotal layers and employs an Elastic Shift Module (ESM) to selectively pull or push layer representations depending on the perturbation types. Extensive experiments across six LLMs and 15 perturbation types demonstrate that EDA achieves state-of-the-art performance on most perturbed benchmarks while preserving reasoning capability on original benchmarks.</abstract>
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%0 Conference Proceedings
%T Learning How to Align: Elastic Domain Adaptation for Improving Reasoning Robustness of Large Language Models
%A Hou, Yujie
%A Wang, Mei
%A Zhong, Yaoyao
%A Li, Guozhang
%A Zhu, Qiannan
%A Huang, Hua
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F hou-etal-2026-learning
%X Despite exceptional performance on static benchmarks, Large Language Models (LLMs) exhibit significant brittleness when evaluated on perturbed variants of the original questions. Current extrinsic interventions, prompt engineering or supervised fine-tuning, overlook an important intrinsic factor underlying this degradation: the distributional shifts in layer activations between original and perturbed questions. To address this, this work formulates the reasoning robustness challenge as an Unsupervised Domain Adaptation (UDA) problem and proposes the Elastic Domain Adaptation (EDA) method. Unlike rigid strategies that enforce a uniform alignment constraint, EDA dynamically aligns layer activations based on the specific layers and perturbation types. Specifically, EDA incorporates an Adaptive Layer Selection (ALS) to identify pivotal layers and employs an Elastic Shift Module (ESM) to selectively pull or push layer representations depending on the perturbation types. Extensive experiments across six LLMs and 15 perturbation types demonstrate that EDA achieves state-of-the-art performance on most perturbed benchmarks while preserving reasoning capability on original benchmarks.
%U https://aclanthology.org/2026.emnlp-main.392/
%P 8995-9018
Markdown (Informal)
[Learning How to Align: Elastic Domain Adaptation for Improving Reasoning Robustness of Large Language Models](https://aclanthology.org/2026.emnlp-main.392/) (Hou et al., EMNLP 2026)
ACL