arXiv AI

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 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.

By Anubhav Jangra, Bahareh Sarrafzadeh, Silviu Cucerzan, Adrian de Wynter, Sujay Kumar Jauhar

llms rag benchmarks

arXiv Computation and Language

4d ago

Fantastic Metrics and How to Find Them: Meta-Evaluating Metrics for Multi-lingual Style-Personalized Generation

arXiv:2610.19166v1 Announce Type: new Abstract: Meta-evaluation of text generation metrics and LLM-judges is primarily conducted using metric-human correlations. However, prior work has shown that co...

By Anubhav Jangra, Adrian de Wynter

llms rag benchmarks

arXiv AI

4d ago

LLMRec: Benchmarking Large Language Models on Recommendation Task

arXiv:2308.12241v2 Announce Type: replace-cross Abstract: Recently, the fast development of Large Language Models (LLMs) such as ChatGPT has significantly advanced NLP tasks by enhancing the capabili...

By Junling Liu, Chao Liu, Peilin Zhou, Qichen Ye, Dading Chong, Kang Zhou, Yueqi Xie, Yuwei Cao, Shoujin Wang, Chenyu You, Philip S. Yu

llms nlp fine-tuning benchmarks

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...

By Huayi Lai, Jicheng Yang, Min Yi, Chong Meng

llms fine-tuning efficiency benchmarks safety

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...

By Yizhuo Chen, Xin Liu, Ruijie Wang, Zheng Li, Pei Chen, Changlong Yu, Qingyu Yin, Priyanka Nigam, Meng Jiang, Bing Yin

llms nlp reinforcement-learning benchmarks

arXiv AI

Jul 29

REPREC: Representation Driven Parameter-Efficient Recommendation System

arXiv:2607. 24845v1 Announce Type: cross Abstract: Large language models (LLMs) have been applied to sequential recommendation by formulating it as a natural language task.

By Harshini Kavuru, Dwipam Katariya, Giri Iyengar, Pranab Mohanty, Kalanand Mishra, Kalanand Mishra

llms rag fine-tuning efficiency benchmarks safety

arXiv AI

Sep 23

CARD: Cluster-level Adaptation with Reward-guided Decoding for Personalized Text Generation

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

llms fine-tuning benchmarks

arXiv AI

Sep 2

Value Over Language Model: Detecting Original Contribution in Writing

The paper introduces VOLM, a framework that quantifies how much original value a human adds to a document beyond what a language model could generate from a task description alone. Unlike existing tools that focus on stylistic detection, VOLM extracts content at varying granularities, reconstructs it with an LLM, and compares these reconstructions to those derived from the task description. Evaluations across news articles, ICLR peer reviews, and argumentative essays show that VOLM can distinguish human-authored texts from LLM-generated ones while remaining robust to content-preserving transformations.

By Vibhhu Sharma, Thorsten Joachims, Sarah Dean

llms

arXiv Machine Learning

Jun 26

GRAG: Generic Response-Augmented Generation Framework for Personalized Conversational Systems

arXiv:2606. 21097v2 Announce Type: replace-cross Abstract: Deploying highly capable personalized conversational agents in resource-constrained or privacy-sensitive environments remains a significant challenge.

By Junfeng Liu, Christopher T. Symons, Ranga Raju Vatsavai

llms agents fine-tuning benchmarks safety

arXiv Machine Learning

Aug 27

Learning to summarize user information for personalized reinforcement learning from human feedback

The paper introduces PLUS, a framework that uses reinforcement learning to generate text-based summaries of individual users’ preferences, characteristics, and past conversations. These summaries condition a reward model, allowing it to predict personalized response preferences and improving reward accuracy by 11–77 % over the standard Bradley‑Terry model. PLUS demonstrates robust performance with new users and topics, achieves a 25 % improvement over existing personalized RLHF techniques, and enables zero‑shot personalization for state‑of‑the‑art models like GPT‑4.

By Hyunji Nam, Yanming Wan, Mickel Liu, Peter Ahnn, Jianxun Lian, Natasha Jaques

llms nlp reinforcement-learning benchmarks safety

arXiv Computation and Language

Sep 10

HyperTrace: Hypothesis-Based Preference Tracing for Online LLM Personalization

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

llms safety

arXiv AI

4d ago

FleetPilot: Fine-Grained Steering Empowers Real-World LLM Personalization

arXiv:2610.14970v1 Announce Type: new Abstract: With the rapid evolution of large language models (LLMs), there is an increasing demand for effective personalization techniques that can adapt model b...

By Kounianhua Du, Jianxing Liu, Kangning Zhang, Wenxiang Jiao, Yuan Lu, Jiarui Jin, Jianghao Lin, Weiwen Liu, Yong Yu, Weinan Zhang

llms agents reinforcement-learning efficiency

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