Step-Back Profiling: Distilling User Interactions for Personalized Scientific Writing
Abstract: Large language models (LLM) excel at a variety of natural language processing tasks, yet they struggle to generate personalized content for individuals, particularly in real-world scenarios like scientific writing. Addressing this challenge, we introduce Step-Back Profiling to personalize LLMs by distilling user history into concise profiles, including essential traits and preferences of users. Regarding our experiments, we construct a Personalized Scientific Writing (PSW) dataset to study multi-user personalization. PSW requires the models to write scientific papers given specialized author groups with diverse academic backgrounds. As for the results, we demonstrate the effectiveness of capturing user characteristics via Step-Back Profiling for collaborative writing. Moreover, our approach outperforms the baselines by up to 3.6 points on the general personalization benchmark (LaMP), including 7 personalization LLM tasks. Our extensive ablation studies validate the contributions of different components in our method and provide insights into our task definition. Our dataset and code will be available upon acceptance.
Paper Type: Short
Research Area: NLP Applications
Research Area Keywords: LLMs, Personalization, Scientific Writing
Contribution Types: NLP engineering experiment, Data resources
Languages Studied: English
Submission Number: 3745
Loading