arXiv AI
CARD: Cluster-level Adaptation with Reward-guided Decoding for Personalized Text Generation
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arXiv AI
Oct 31
From Profiles to Steering Vectors: Global Sparse Priors and Local Semantic Calibration for Personalized Text Generation
The paper introduces GLASS, a training‑free framework that personalizes text generation by combining global user‑style priors extracted with sparse autoencoders and local contrastive style vectors built from clustered interaction scenarios. During inference, GLASS injects these global and local vectors into different model layers, achieving context‑aware personalization without retrieval or parameter updates. Experiments on LaMP and LongLaMP datasets demonstrate that GLASS surpasses retrieval, fine‑tuning, and other steering baselines on ROUGE metrics and LLM‑as‑judge evaluations, and its SAE‑based representations show robustness to topic and length shifts, indicating better disentanglement of style from content.
arXiv AI
Oct 15
Iterative Critique-Refine Framework for Enhancing LLM Personalization
The paper introduces PerFine, a training‑free critique‑refine framework that improves large language model personalization. In each iteration, a generator drafts text conditioned on a retrieved user profile, while a critic—also conditioned on the same profile—provides structured feedback on tone, vocabulary, sentence structure, and topicality. The generator revises the draft, and a knockout strategy selects the best version across iterations; additional inference‑time techniques such as Best‑of‑N and Topic Extraction further balance quality and efficiency. Experiments on Yelp, Goodreads, and Amazon datasets show PerFine consistently outperforms existing retrieval‑augmented methods like PGraphRAG, achieving G‑Eval gains of +8.5‑12% and steady improvements over 3‑5 refinement iterations.
arXiv AI
Jul 14
Do Implicit Personalization and Explicit Styles Conflict? PsPLUG: A Lightweight Plug-in for Balancing Personalization and Style in Customized LLMs
arXiv:2601. 06362v2 Announce Type: replace Abstract: Personalized large language models are often expected to follow explicit style instructions, yet we find that such instructions can undermine the user-specific characteristics that personalization methods aim to preserve.
arXiv Computation and Language
Sep 2
Evaluating Style-Personalized Text Generation: Challenges and Directions
The paper "Evaluating Style-Personalized Text Generation: Challenges and Directions" examines the difficulties of assessing text that is tailored to individual users’ styles. It critiques common metrics such as BLEU, embeddings, and LLM-as-judges, and introduces a style discrimination benchmark covering domain discrimination, authorship attribution, and LLM-generated personalized versus non-personalized discrimination across eight writing tasks. The study finds that ensembles of diverse evaluation metrics outperform single-evaluator approaches and offers guidance for reliable assessment of style-personalized generation.
arXiv Computation and Language
Sep 23
POPI: Personalizing LLMs via Optimized Natural Language Preference Inference
arXiv:2510.17881v4 Announce Type: replace Abstract: Large language models (LLMs) are typically aligned with population-level preferences, despite substantial variation across individual users. We int...
arXiv Computation and Language
Oct 8
MIRROR: From Imitation to Internalization in LLM Personalization
arXiv:2610.09795v1 Announce Type: new Abstract: The demand for personalized LLMs is shifting from style imitation toward content quality. We investigate whether self-distillation can bridge this gap...