arXiv:2601.06352v3 Announce Type: replace
Abstract: Adapting large language models to individual users remains challenging due to the tension between fine-grained personalization and scalable deploym...
By Yutong Song, Jiang Wu, Weijia Zhang, Chengze Shen, Shaofan Yuan, Weitao Lu, Jian Wang, Yu Wang, Nikil Dutt, Amir M. Rahmani
The paper "Efficient Personalization of Generative User Interfaces" addresses the challenge of tailoring generative user interfaces (GenUIs) to individual users when interface screens are not pre‑defined. By collecting judgments from 20 participants on 600 GenUI pairs, the authors show low agreement (Krippendorff's alpha = 0.25) and diverse rationales for UI preferences. They propose a sample‑efficient personalization method that leverages a few pairwise judgments to weight prior users’ preferences, outperforming a pretrained UI evaluator and a larger multimodal model offline and outperforming all baselines in an online study with 12 new users.
"whyItMatters":"The study demonstrates a practical approach to personalizing on‑demand interfaces, showing that even sparse, subjective feedback can be effectively used to improve user satisfaction with generative UI designs."
By Yi-Hao Peng, Jeffrey P. Bigham, Jason Wu
HyperTrace is a training‑free framework that personalizes large language models by tracing latent user preferences online. It maintains interpretable natural‑language hypotheses about short‑term intent and long‑term preferences, updating them with an SMC‑style reweighting process driven by an LLM‑based surrogate choice model. Experiments on PRISM and PersonaMem‑v2 demonstrate that HyperTrace improves response alignment, preference prediction, and profile consistency compared to strong online baselines.
By Jianzhi Shen, Keyu Mao, Minghao Shao, Chuanyang Jin, Yusong Wang, Ailiang Lin, Kotaro Funakoshi, Manabu Okumura, Tianmin Shu, Muhammad Shafique
arXiv:2505. 04260v3 Announce Type: replace-cross Abstract: Personalizing LLM responses typically requires users to articulate their preferences through prompting, which can be burdensome at cold start and difficult to articulate in natural language.
By Jessica Y. Bo, Tianyu Xu, Ishan Chatterjee, Katrina Passarella-Ward, Achin Kulshrestha, D Shin
arXiv:2608.28833v1 Announce Type: new
Abstract: While Large language models (LLMs) incorporate user personalization signals to improve usability and helpfulness, they increasingly shift from providin...
By Yumeng Wang, Yuchen Wu, Cheng Qian, Zhiyuan Fan, Hyeonjeong Ha, Shujin Wu, Jiayu Liu, Heng Ji, Ge Wang
arXiv:2608. 11735v1 Announce Type: cross Abstract: Large language models (LLMs) often shift their outputs in response to implicit demographic cues even when users never state a demographic identity.
By Yueru Yan, Siqi Wu, Thai Le