Open-source AquiLLM framework aids research groups in capturing tacit knowledge

Researchers have developed AquiLLM, an open-source framework designed to help research groups capture tacit knowledge using retrieval-augmented generation (RAG) and large language models (LLMs). The system emphasizes transparency, reproducibility, and privacy by utilizing open-weight models and offering local embedding and multimodal capabilities. AquiLLM aims to better align AI systems with scientific research practices, incorporating feedback from astrophysicists and environmental researchers. AI

IMPACT This open-source framework could improve knowledge sharing and reproducibility in scientific research by providing a transparent and customizable AI tool.

RANK_REASON The item is a research paper detailing a new architecture for an AI system. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• alphaXiv
• AquiLLM
• arXiv
• Astrophysicists’ Conversational Connections on Twitter
• CatalyzeX Code Finder for Papers
• Connected Papers
• CORE Recommender
• DagsHub
• Environmental Researchers’ Data Practices: An Exploratory Study in Turkey
• Gotit.pub
• Hugging Face
• Influence Flower
• large language models
• Litmaps
• OpenAI
• retrieval-augmented generation
• ScienceCast
• scite Smart Citations

• paper
• product

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →