Summary of Rag Foundry: a Framework For Enhancing Llms For Retrieval Augmented Generation, by Daniel Fleischer et al.
RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation
by Daniel Fleischer, Moshe Berchansky, Moshe Wasserblat, Peter Izsak
First submitted to arxiv on: 5 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces RAG Foundry, an open-source framework for implementing Retrieval-Augmented Generation (RAG) systems. This framework integrates data creation, training, inference, and evaluation into a single workflow, enabling rapid prototyping and experimentation with various RAG techniques. The authors demonstrate the effectiveness of the framework by augmenting and fine-tuning Llama-3 and Phi-3 models with diverse RAG configurations, showcasing consistent improvements across three knowledge-intensive datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RAG Foundry is a tool that helps computers generate text based on what they’ve learned from data. It’s like having a super smart assistant who can help you write emails, articles, or even entire books! The team behind this project made it easy to use and test different ways of generating text. They even showed how well it works by using two special models, Llama-3 and Phi-3, with different settings and seeing the results. |
Keywords
» Artificial intelligence » Fine tuning » Inference » Llama » Rag » Retrieval augmented generation
