← Back to all news
arXiv AI By Xinyu Li, Ruoming Jin, Jianfeng Zhu, Ruixin Guo, Zhi Liu

Aplaud: Adaptive Personalized Low-Rank Decomposition for User-Specific LLM

Read the original on arXiv AI →

The paper introduces Aplaud, a lightweight framework that personalizes large language models for survey response prediction. Aplaud builds on LoRA by separating adaptation into a shared low‑rank basis, a compact user‑specific correction, and a rank‑one residual for finer personalization. Experiments show that Aplaud outperforms existing LoRA‑based methods in both generalization and inference efficiency while keeping per‑user parameter costs low.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv AI
Sep 7

PLUME: Parameter-Efficient Personalization of Large Language Models via Low-Rank User Modulation in Shared Subspaces

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
llmsfine-tuningbenchmarks
More like this →
arXiv Computation and Language
Sep 3

Beyond Retrieval: Learning Compact User Representations for Scalable LLM Personalization

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
llmsrag
More like this →
arXiv AI
Sep 2

Breaking the Structural Identity: Personalized Federated LoRA Fine-tuning under Rank Heterogeneity

The paper introduces FedRoRA, a federated learning framework that combines Low‑Rank Adaptation (LoRA) with rank‑heterogeneous personalization. It separates model adaptation into shared global directions and client‑specific rank‑wise magnitudes, using SVD on the server to extract a global subspace and a personalized projection with top‑k selection for each client. Experiments on natural language understanding and generation tasks show that FedRoRA outperforms existing state‑of‑the‑art methods.

By Lei Wang, Jieming Bian, Letian Zhang, Jie Xu
llmsfine-tuningbenchmarkssafety
More like this →