@inproceedings{song-etal-2026-card,
title = "{CARD}: Cluster-level Adaptation with Reward-guided Decoding for Personalized Text Generation",
author = "Song, Yutong and
Wu, Jiang and
Zhang, Weijia and
Shen, Chengze and
Yuan, Shaofan and
Lu, Weitao and
Wang, Jian and
Wang, Yu and
Dutt, Nikil and
Rahmani, Amir M.",
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.44/",
pages = "889--908",
ISBN = "979-8-89176-462-0",
abstract = "Adapting large language models to individual users remains challenging because systems must capture fine-grained personal preferences while serving many users efficiently. We introduce CARD, a hierarchical personalization framework that first learns preferences shared across user groups and then captures how individual users depart from those shared patterns. CARD trains a parameter-efficient adaptation module for each group, providing a reliable prior even when a user has limited history. It then learns a compact user preference vector by contrasting user-authored responses with outputs from the corresponding group model, without requiring manual annotations. During inference, both the backbone and the group adaptation module remain frozen, while the user vector applies a low-rank correction to the token logits during decoding. Experiments on personalization benchmarks show that CARD consistently outperforms competitive baselines while requiring only a compact vector for each user."
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<abstract>Adapting large language models to individual users remains challenging because systems must capture fine-grained personal preferences while serving many users efficiently. We introduce CARD, a hierarchical personalization framework that first learns preferences shared across user groups and then captures how individual users depart from those shared patterns. CARD trains a parameter-efficient adaptation module for each group, providing a reliable prior even when a user has limited history. It then learns a compact user preference vector by contrasting user-authored responses with outputs from the corresponding group model, without requiring manual annotations. During inference, both the backbone and the group adaptation module remain frozen, while the user vector applies a low-rank correction to the token logits during decoding. Experiments on personalization benchmarks show that CARD consistently outperforms competitive baselines while requiring only a compact vector for each user.</abstract>
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%0 Conference Proceedings
%T CARD: Cluster-level Adaptation with Reward-guided Decoding for Personalized Text Generation
%A Song, Yutong
%A Wu, Jiang
%A Zhang, Weijia
%A Shen, Chengze
%A Yuan, Shaofan
%A Lu, Weitao
%A Wang, Jian
%A Wang, Yu
%A Dutt, Nikil
%A Rahmani, Amir M.
%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 song-etal-2026-card
%X Adapting large language models to individual users remains challenging because systems must capture fine-grained personal preferences while serving many users efficiently. We introduce CARD, a hierarchical personalization framework that first learns preferences shared across user groups and then captures how individual users depart from those shared patterns. CARD trains a parameter-efficient adaptation module for each group, providing a reliable prior even when a user has limited history. It then learns a compact user preference vector by contrasting user-authored responses with outputs from the corresponding group model, without requiring manual annotations. During inference, both the backbone and the group adaptation module remain frozen, while the user vector applies a low-rank correction to the token logits during decoding. Experiments on personalization benchmarks show that CARD consistently outperforms competitive baselines while requiring only a compact vector for each user.
%U https://aclanthology.org/2026.findings-emnlp.44/
%P 889-908
Markdown (Informal)
[CARD: Cluster-level Adaptation with Reward-guided Decoding for Personalized Text Generation](https://aclanthology.org/2026.findings-emnlp.44/) (Song et al., Findings 2026)
ACL
- Yutong Song, Jiang Wu, Weijia Zhang, Chengze Shen, Shaofan Yuan, Weitao Lu, Jian Wang, Yu Wang, Nikil Dutt, and Amir M. Rahmani. 2026. CARD: Cluster-level Adaptation with Reward-guided Decoding for Personalized Text Generation. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 889–908, Budapest, Hungary. Association for Computational Linguistics.