@inproceedings{yang-etal-2026-diper,
title = "{D}i{P}er: Disentangled Personal Direction Discovery for Personalized Generation Steering",
author = "Yang, Muchen and
Gan, Weinan and
Shi, Wentao and
Gao, Chongming and
Dai, Quanyu and
Wang, Yichao and
Liu, Yong and
Li, Yangyang and
Feng, Fuli",
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.1865/",
pages = "39810--39826",
ISBN = "979-8-89176-462-0",
abstract = "Large Language Model (LLM) personalization aims to adapt generation to individual users by capturing implicit preferences from historical interactions. Steering-based methods offer an inference-efficient solution, but existing work follows a difference-based paradigm that estimates personalized steering directions from representation offsets between contrastive historical examples, thus entangling inherent user preference with preference-agnostic contextual bias. This limitation arises because offset-based estimation uses raw contrastive variation, making the derived direction confounded by contextual bias rather than identifying preference-discriminative variation between user-conditioned and generic behavior. To address this, we propose \textbf{Disentangled Personal Direction Discovery (DiPer)}, which formulates personalized steering vector estimation as a discriminative direction discovery problem. DiPer learns layer-wise user-specific directions through supervised metric learning, based on Recursive Feature Machine (CITATION), over historical activations, to discover preference-discriminative directions while suppressing contextual bias. Theoretical analysis and extensive experiments demonstrate DiPer{'}s efficacy and robustness."
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<abstract>Large Language Model (LLM) personalization aims to adapt generation to individual users by capturing implicit preferences from historical interactions. Steering-based methods offer an inference-efficient solution, but existing work follows a difference-based paradigm that estimates personalized steering directions from representation offsets between contrastive historical examples, thus entangling inherent user preference with preference-agnostic contextual bias. This limitation arises because offset-based estimation uses raw contrastive variation, making the derived direction confounded by contextual bias rather than identifying preference-discriminative variation between user-conditioned and generic behavior. To address this, we propose Disentangled Personal Direction Discovery (DiPer), which formulates personalized steering vector estimation as a discriminative direction discovery problem. DiPer learns layer-wise user-specific directions through supervised metric learning, based on Recursive Feature Machine (CITATION), over historical activations, to discover preference-discriminative directions while suppressing contextual bias. Theoretical analysis and extensive experiments demonstrate DiPer’s efficacy and robustness.</abstract>
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%0 Conference Proceedings
%T DiPer: Disentangled Personal Direction Discovery for Personalized Generation Steering
%A Yang, Muchen
%A Gan, Weinan
%A Shi, Wentao
%A Gao, Chongming
%A Dai, Quanyu
%A Wang, Yichao
%A Liu, Yong
%A Li, Yangyang
%A Feng, Fuli
%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 yang-etal-2026-diper
%X Large Language Model (LLM) personalization aims to adapt generation to individual users by capturing implicit preferences from historical interactions. Steering-based methods offer an inference-efficient solution, but existing work follows a difference-based paradigm that estimates personalized steering directions from representation offsets between contrastive historical examples, thus entangling inherent user preference with preference-agnostic contextual bias. This limitation arises because offset-based estimation uses raw contrastive variation, making the derived direction confounded by contextual bias rather than identifying preference-discriminative variation between user-conditioned and generic behavior. To address this, we propose Disentangled Personal Direction Discovery (DiPer), which formulates personalized steering vector estimation as a discriminative direction discovery problem. DiPer learns layer-wise user-specific directions through supervised metric learning, based on Recursive Feature Machine (CITATION), over historical activations, to discover preference-discriminative directions while suppressing contextual bias. Theoretical analysis and extensive experiments demonstrate DiPer’s efficacy and robustness.
%U https://aclanthology.org/2026.findings-emnlp.1865/
%P 39810-39826
Markdown (Informal)
[DiPer: Disentangled Personal Direction Discovery for Personalized Generation Steering](https://aclanthology.org/2026.findings-emnlp.1865/) (Yang et al., Findings 2026)
ACL
- Muchen Yang, Weinan Gan, Wentao Shi, Chongming Gao, Quanyu Dai, Yichao Wang, Yong Liu, Yangyang Li, and Fuli Feng. 2026. DiPer: Disentangled Personal Direction Discovery for Personalized Generation Steering. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 39810–39826, Budapest, Hungary. Association for Computational Linguistics.