The paper investigates how to personalize Large Language Models by leveraging Mixture-of-Experts (MoE) architectures, which route tokens to a few specialized experts, thereby reducing polysemanticity compared to dense models. The authors adapt three open-weight MoE LLMs on a large personalization dataset, train crosscoders to learn interpretable personalized latent representations, and identify experts that encode distinct personalization features. Experiments show that personalized MoE LLMs match dense LLM performance while activating fewer parameters, exhibit lower polysemanticity, and that causal interventions on the identified experts can steer a non-personalized model toward personalized behavior.
By Sekh Mainul Islam, Pepa Atanasova, Isabelle Augenstein
arXiv:2606. 06614v1 Announce Type: cross Abstract: Despite growing interest, most evaluations of large language models' (LLMs') personalization abilities have relied on synthetic data.
By Lechen Zhang, Jiarui Liu, Tal August
arXiv:2610.08330v1 Announce Type: new
Abstract: Proprietary Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, yet aligning their outputs with divers...
By Hun Park
The paper investigates how to make Mixture-of-Experts (MoE) reward models more interpretable and useful for personalization by focusing on sparsity and diversity regularizers. By keeping the architecture and training data constant, the authors show that conventional MoE reward models learn entangled components that hinder both interpretability and personalization. Introducing a sparse MoE objective reduces entanglement, yielding experts that align with semantic categories and functional strengths, and enables composable, few‑shot personalization with a 25.81‑point improvement over entangled models.
By Yifan Wang, Jinyi Mu, Mayank Jobanputra, Yu Wang, Ji-Ung Lee, Soyoung Oh, Isabel Valera, Vera Demberg
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
PLUME is a lightweight framework for personalizing large language models by learning a shared task-specific subspace from aggregated user data and then training only a small square matrix for each user within that subspace. The approach introduces cross-layer shared parameters and rank‑1 residual terms to reduce redundancy while preserving expressiveness. Experiments on personalized text generation benchmarks show that PLUME matches or outperforms strong baselines while cutting per‑user parameters by over 95%.
By Xinyu Li, Hao Zhou, Jianfeng Zhu, Julina Maharjan, Ruixin Guo, Feodor Dragan, Ruoming Jin
The paper introduces TAP-PER, a prefix‑based framework that learns compact user representations for large language model personalization. By encoding user preferences into lightweight prefix embeddings and incorporating temporal signals, TAP‑PER avoids the need for heavy per‑user adapters or prompt‑serialized histories. Experiments on six LaMP tasks show that TAP‑PER outperforms both prompt‑based and model‑based baselines while using far fewer per‑user parameters, enabling scalable personalization at large user scales.
By Heng Cao, Fan Zhang, Jian Yao, Yujie Zheng, Changlin Zhao, Lu Hao, Yuxuan Wei, Wangze Ni, Huaiyu Fu, Yuqian Sun, Xuyan Mo
arXiv:2606. 17276v1 Announce Type: cross Abstract: Generative recommendation (GR) has emerged as a promising direction for recommender systems.
By Sunwoo Kim, Sunkyung Lee, Clark Mingxuan Ju, Donald Loveland, Bhuvesh Kumar, Kijung Shin, Neil Shah, Liam Collins
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:2606. 04284v1 Announce Type: cross Abstract: Preference modeling plays a central role in reinforcement learning from human feedback (RLHF), enabling large language models (LLMs) to align with human values.
By Yifan Wang, Jinyi Mu, Mayank Jobanputra, Yu Wang, Ji-Ung Lee, Soyoung Oh, Isabel Valera, Vera Demberg
Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior. However, the limited quality of item representations remains a critical bottleneck.
arXiv:2608. 12389v1 Announce Type: new Abstract: Cross-domain zero- or few-shot personalization aims to generate user-preferred responses in unseen conversational domains from only a handful of target-domain interactions.
By Xuefei Wang, Jun Han, Zixuan Wang, Qingkai Zeng, Xiao Wang, Ruijie Wang, Jianxin Li