Summary of Ragprobe: An Automated Approach For Evaluating Rag Applications, by Shangeetha Sivasothy et al.
RAGProbe: An Automated Approach for Evaluating RAG Applications
by Shangeetha Sivasothy, Scott Barnett, Stefanus Kurniawan, Zafaryab Rasool, Rajesh Vasa
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 This paper proposes a technique to generate variations in question-answer pairs to evaluate Retrieval Augmented Generation (RAG) pipelines. The current evaluation process relies on manual trial and error, which is time-consuming and prone to errors. The authors validate their approach using five open-source RAG pipelines and three datasets, finding that prompts combining multiple questions lead to the highest failure rates. They also demonstrate that their automated method outperforms existing state-of-the-art approaches by increasing the failure rate by 51% on average per dataset. This work presents an automated approach for continuously monitoring the health of RAG pipelines, which can be integrated into existing CI/CD pipelines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RAG is a type of Generative AI that helps build applications. Right now, people evaluate these applications and pipelines manually, which takes a lot of time and can be tricky. The authors of this paper want to make it easier by creating a system that generates different types of questions and answers to test the pipelines. They tested their idea using five different open-source RAG pipelines and three datasets. What they found is that when you ask multiple questions at once, the pipeline has trouble answering them correctly. This is important because it shows that developers need to make sure their pipelines can handle these kinds of questions. The authors’ method also works better than what’s already available. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
