arXiv AI August 7, 2026 By Gihoon Kim, Jeyoung Lee, Suhan Woo, Sekwon Oh, Minsu Jeon, Hyounsoo Han, Euntai Kim

Cautious Context Steering for Language Model Personalization

arXiv:2608. 05813v1 Announce Type: new Abstract: Personalizing language models (LMs) to individual user preferences is essential for aligning responses with diverse goals and backgrounds.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Aug 11

Learning Preference Adaptation for Large Language Model Personalization via Verbal Reinforcement Learning

arXiv:2608. 09507v1 Announce Type: cross Abstract: Natural language user preferences provide an interpretable interface for LLM personalization.

By Yuting Liu, Wei Wu, Jianzhe Zhao, Guibing Guo

llms rag agents reinforcement-learning

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 AI

Jun 9

Emergence of Context Characteristics Sensitivity in Large Language Models

arXiv:2606. 09525v1 Announce Type: cross Abstract: During instruction fine-tuning (IFT), large language models (LLMs) learn to follow instructions by using the provided context to answer a query.

By Nadya Yuki Wangsajaya, Haeun Yu, Isabelle Augenstein

llms reinforcement-learning fine-tuning

arXiv AI

Jul 7

Context Tuning for In-Context Optimization

arXiv:2507. 04221v3 Announce Type: replace-cross Abstract: We introduce Context Tuning, a simple and effective method to significantly enhance few-shot adaptation of large language models (LLMs) without weight updates.

By Jack Lu, Ryan Teehan, Zhenbang Yang, Mengye Ren

llms benchmarks

arXiv Machine Learning

Jun 30

Synthetic Interaction Data for Scalable Personalization in Large Language Models

arXiv:2602. 12394v2 Announce Type: replace Abstract: Personalized prompting offers large opportunities for deploying large language models (LLMs) to diverse users, yet existing prompt optimization methods primarily focus on task-level optimization while largely overlooking user-specific preferences and latent constraints of individual users.

By Yuchen Ma, Yue Huang, Wenjie Wang, Xiaonan Luo, Xiangliang Zhang, Stefan Feuerriegel

llms agents reinforcement-learning benchmarks safety

arXiv AI

Jul 28

Frustratingly Simple Black-Box Adaptation of Language Models via Logit Bias

arXiv:2607. 22837v1 Announce Type: cross Abstract: Many organizations aim to adapt language models for internal use, both to improve performance on domain-specific tasks and to address privacy concerns around sensitive data.

By Ofek I. Cohen, Lior Shani, Aviv Rosenberg, Ankur Samanta, Tal Wagner, Yonathan Efroni

llms reinforcement-learning fine-tuning benchmarks safety

Related stories

Learning Preference Adaptation for Large Language Model Personalization via Verbal Reinforcement Learning

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

Emergence of Context Characteristics Sensitivity in Large Language Models

Context Tuning for In-Context Optimization

Synthetic Interaction Data for Scalable Personalization in Large Language Models

Frustratingly Simple Black-Box Adaptation of Language Models via Logit Bias