Summary of The Chronicles Of Rag: the Retriever, the Chunk and the Generator, by Paulo Finardi et al.
The Chronicles of RAG: The Retriever, the Chunk and the Generator
by Paulo Finardi, Leonardo Avila, Rodrigo Castaldoni, Pedro Gengo, Celio Larcher, Marcos Piau, Pablo Costa, Vinicius Caridá
First submitted to arxiv on: 15 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR)
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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 presents good practices for implementing, optimizing, and evaluating Retrieval Augmented Generation (RAG) models for the Brazilian Portuguese language. Specifically, it focuses on establishing a simple pipeline for inference and experiments. The authors explore various methods to answer questions about the first Harry Potter book using OpenAI’s gpt-4, gpt-4-1106-preview, gpt-3.5-turbo-1106, and Google’s Gemini Pro models. They achieve an improvement of 35.4% in MRR@10 compared to the baseline by focusing on the quality of the retriever. The authors also optimize the input size and observe a further enhancement of 2.4%. Finally, they present the complete architecture of the RAG model with their recommendations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RAG is a way for machines to learn from external data. It’s like a superpower that helps them be more accurate and knowledgeable. The problem is that it can be hard to set up and use. This paper shows how to make it work better by following some simple steps. They tested different methods using famous book questions and achieved great results. By improving the way they search for answers, they were able to get 35.4% better than before! They also found that tweaking the input size can give an extra boost of 2.4%. The paper shares its findings in a clear and easy-to-understand way. |
Keywords
* Artificial intelligence * Gemini * Gpt * Inference * Rag * Retrieval augmented generation
