9 papers
WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback
Taiwei Shi, Zhuoer Wang, Longqi Yang +12
As large language models (LLMs) continue to advance, aligning these models with human preferences has emerged as a critical challenge. Traditional alignment methods, relying on hum…
DP-RFT: Learning to Generate Synthetic Text via Differentially Private Reinforcement Fine-Tuning
Fangyuan Xu, Sihao Chen, Zinan Lin +13
Differentially private (DP) synthetic data generation plays a pivotal role in developing large language models (LLMs) on private data, where data owners cannot provide eyes-on acce…
Group Preference Alignment: Customized LLM Response Generation from In-Situ Conversations
Ishani Mondal, Jack W. Stokes, Sujay Kumar Jauhar +5
LLMs often fail to meet the specialized needs of distinct user groups due to their one-size-fits-all training paradigm \cite{lucy-etal-2024-one} and there is limited research on wh…
GenTool: Enhancing Tool Generalization in Language Models through Zero-to-One and Weak-to-Strong Simulation
Jie He, Jennifer Neville, Mengting Wan +6
Large Language Models (LLMs) can enhance their capabilities as AI assistants by integrating external tools, allowing them to access a wider range of information. While recent LLMs…
Pearl: Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retrievers
Sheshera Mysore, Zhuoran Lu, Mengting Wan +7
Powerful large language models have facilitated the development of writing assistants that promise to significantly improve the quality and efficiency of composition and communicat…
Interpretable User Satisfaction Estimation for Conversational Systems with Large Language Models
Ying-Chun Lin, Jennifer Neville, Jack W. Stokes +14
Accurate and interpretable user satisfaction estimation (USE) is critical for understanding, evaluating, and continuously improving conversational systems. Users express their sati…