AI PERSONA introduces a training-free, prompt-based framework that maintains and updates per-user persona profiles, plus a synthetic benchmark on which it outperforms no-persona and RAG baselines, approaching the oracle golden-persona setting.
Paper Copilot: A Self-Evolving and Efficient LLM System for Personalized Academic Assistance
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abstract
As scientific research proliferates, researchers face the daunting task of navigating and reading vast amounts of literature. Existing solutions, such as document QA, fail to provide personalized and up-to-date information efficiently. We present Paper Copilot, a self-evolving, efficient LLM system designed to assist researchers, based on thought-retrieval, user profile and high performance optimization. Specifically, Paper Copilot can offer personalized research services, maintaining a real-time updated database. Quantitative evaluation demonstrates that Paper Copilot saves 69.92\% of time after efficient deployment. This paper details the design and implementation of Paper Copilot, highlighting its contributions to personalized academic support and its potential to streamline the research process.
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AI PERSONA: Towards Life-long Personalization of LLMs
AI PERSONA introduces a training-free, prompt-based framework that maintains and updates per-user persona profiles, plus a synthetic benchmark on which it outperforms no-persona and RAG baselines, approaching the oracle golden-persona setting.