PyZoBot: A Platform for Conversational Information Extraction and Synthesis from Curated Zotero Reference Libraries through Advanced Retrieval-Augmented Generation

التفاصيل البيبلوغرافية
العنوان: PyZoBot: A Platform for Conversational Information Extraction and Synthesis from Curated Zotero Reference Libraries through Advanced Retrieval-Augmented Generation
المؤلفون: Alshammari, Suad, Basalelah, Lama, Rukbah, Walaa Abu, Alsuhibani, Ali, Wijesinghe, Dayanjan S.
سنة النشر: 2024
المجموعة: Computer Science
مصطلحات موضوعية: Computer Science - Human-Computer Interaction
الوصف: The exponential growth of scientific literature has resulted in information overload, challenging researchers to effectively synthesize relevant publications. This paper explores the integration of traditional reference management software with advanced computational techniques, including Large Language Models and Retrieval-Augmented Generation. We introduce PyZoBot, an AI-driven platform developed in Python, incorporating Zoteros reference management with OpenAIs sophisticated LLMs. PyZoBot streamlines knowledge extraction and synthesis from extensive human-curated scientific literature databases. It demonstrates proficiency in handling complex natural language queries, integrating data from multiple sources, and meticulously presenting references to uphold research integrity and facilitate further exploration. By leveraging LLMs, RAG, and human expertise through a curated library, PyZoBot offers an effective solution to manage information overload and keep pace with rapid scientific advancements. The development of such AI-enhanced tools promises significant improvements in research efficiency and effectiveness across various disciplines.
Comment: 10 pages, 2 figures. The code is provided in github and the link to the repository is provided at the end of the publication
نوع الوثيقة: Working Paper
URL الوصول: http://arxiv.org/abs/2405.07963
رقم الأكسشن: edsarx.2405.07963
قاعدة البيانات: arXiv