Summary of Towards Optimizing a Retrieval Augmented Generation Using Large Language Model on Academic Data, by Anum Afzal et al.
Towards Optimizing a Retrieval Augmented Generation using Large Language Model on Academic Data
by Anum Afzal, Juraj Vladika, Gentrit Fazlija, Andrei Staradubets, Florian Matthes
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 A novel study assesses Retrieval Augmented Generation (RAG) models on domain-specific data, exploring four optimizations to enhance their functionality and performance. The research focuses on data retrieval for various study programs at a large technical university, introducing a new evaluation approach, the RAG Confusion Matrix. By integrating open-source and closed-source Large Language Models, such as Llama2, Mistral, GPT-3.5, and GPT-4, the study offers valuable insights into optimizing RAG frameworks in domain-specific contexts. The results show a significant performance increase when incorporating multi-query in the retrieval phase. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how organizations can use a technology called Retrieval Augmented Generation (RAG) to make their work better. They tested different ways to make this technology work, using data from a big technical university. They also came up with a new way to measure how well RAG works. By combining different types of computer models, the researchers learned more about how to make RAG work best in different situations. The results showed that making some changes can really improve how well RAG performs. |
Keywords
» Artificial intelligence » Confusion matrix » Gpt » Rag » Retrieval augmented generation
