7 papers
Instant Personalized Large Language Model Adaptation via Hypernetwork
Zhaoxuan Tan, Zixuan Zhang, Haoyang Wen +8
Personalized large language models (LLMs) tailor content to individual preferences using user profiles or histories. However, existing parameter-efficient fine-tuning (PEFT) method…
POPI: Personalizing LLMs via Optimized Natural Language Preference Inference
Yizhuo Chen, Xin Liu, Ruijie Wang +7
Large language models (LLMs) are typically aligned with population-level preferences, despite substantial variation across individual users. We introduce POPI, a user-level persona…
Self-Rewarding PPO: Aligning Large Language Models with Demonstrations Only
Qingru Zhang, Liang Qiu, Ilgee Hong +11
Supervised fine-tuning (SFT) has emerged as a crucial method for aligning large language models (LLMs) with human-annotated demonstrations. However, SFT, being an off-policy approa…
Aligning Large Language Models with Implicit Preferences from User-Generated Content
Zhaoxuan Tan, Zheng Li, Tianyi Liu +10
Learning from preference feedback is essential for aligning large language models (LLMs) with human values and improving the quality of generated responses. However, existing prefe…
IHEval: Evaluating Language Models on Following the Instruction Hierarchy
Zhihan Zhang, Shiyang Li, Zixuan Zhang +11
The instruction hierarchy, which establishes a priority order from system messages to user messages, conversation history, and tool outputs, is essential for ensuring consistent an…
Personalized News Recommendation System via LLM Embedding and Co-Occurrence Patterns
Zheng Li, Kai Zhange
In the past two years, large language models (LLMs) have achieved rapid development and demonstrated remarkable emerging capabilities. Concurrently, with powerful semantic understa…