Summary of Ragbench: Explainable Benchmark For Retrieval-augmented Generation Systems, by Robert Friel et al.
RAGBench: Explainable Benchmark for Retrieval-Augmented Generation Systems
by Robert Friel, Masha Belyi, Atindriyo Sanyal
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 Retrieval-Augmented Generation (RAG) Bench, a comprehensive large-scale dataset for evaluating RAG systems that incorporate domain-specific knowledge into user-facing chat applications powered by Large Language Models. The benchmark includes 100k examples across five unique industry-specific domains and various task types. RAGBench is sourced from industry corpora such as user manuals, making it relevant for industry applications. The paper also formalizes the TRACe evaluation framework, a set of explainable and actionable metrics applicable across all RAG domains. Additionally, the authors release the labeled dataset at this URL. The paper’s main contribution is the introduction of RAGBench and the TRACe evaluation framework, which facilitates holistic evaluation of RAG systems and enables actionable feedback for continuous improvement. The authors also find that LLM-based RAG evaluation methods struggle to compete with a finetuned RoBERTa model on the RAG evaluation task. The paper’s findings highlight areas where existing approaches fall short and propose the adoption of RAGBench with TRACe towards advancing the state of RAG evaluation systems. The authors’ work has implications for the development of production applications that leverage large language models for conversational AI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RAG is a new way to make chatbots better by using special knowledge from a specific industry or area. Right now, it’s hard to compare how well different RAG systems are working because there isn’t one set of rules and data to use. The researchers created a huge dataset called RAGBench that has 100,000 examples across five different industries like healthcare or finance. They also made a new way to measure how good an RAG system is by using something called TRACe. The goal of this project is to make it easier for developers to create better chatbots by giving them a set of rules and data to work with. This will help make sure that the chatbots are useful and accurate in different situations. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
