PyZoBot: A Platform for Conversational Information Extraction and Synthesis from Curated Zotero Reference Libraries through Advanced Retrieval-Augmented Generation
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866908508884041728 |
|---|---|
| author | Alshammari, Suad Basalelah, Lama Rukbah, Walaa Abu Alsuhibani, Ali Wijesinghe, Dayanjan S. |
| author_facet | Alshammari, Suad Basalelah, Lama Rukbah, Walaa Abu Alsuhibani, Ali Wijesinghe, Dayanjan S. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_07963 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PyZoBot: A Platform for Conversational Information Extraction and Synthesis from Curated Zotero Reference Libraries through Advanced Retrieval-Augmented Generation Alshammari, Suad Basalelah, Lama Rukbah, Walaa Abu Alsuhibani, Ali Wijesinghe, Dayanjan S. Human-Computer Interaction 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. |
| title | PyZoBot: A Platform for Conversational Information Extraction and Synthesis from Curated Zotero Reference Libraries through Advanced Retrieval-Augmented Generation |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2405.07963 |