3 papers
cs.AI2026
Learning Personalized Agents from Human Feedback
Kaiqu Liang, Julia Kruk, Shengyi Qian +9
Modern AI agents are powerful but often fail to align with the idiosyncratic, evolving preferences of individual users. Prior approaches typically rely on static datasets, either t…
cs.AI2024
Robust Multi-bit Text Watermark with LLM-based Paraphrasers
Xiaojun Xu, Jinghan Jia, Yuanshun Yao +2
We propose an imperceptible multi-bit text watermark embedded by paraphrasing with LLMs. We fine-tune a pair of LLM paraphrasers that are designed to behave differently so that the…
cs.CL2024
ACC-Collab: An Actor-Critic Approach to Multi-Agent LLM Collaboration
Andrew Estornell, Jean-Francois Ton, Yuanshun Yao +1
Large language models (LLMs) have demonstrated a remarkable ability to serve as general-purpose tools for various language-based tasks. Recent works have demonstrated that the effi…