Summary of Evaluation Of Retrieval-augmented Generation: a Survey, by Hao Yu et al.
Evaluation of Retrieval-Augmented Generation: A Survey
by Hao Yu, Aoran Gan, Kai Zhang, Shiwei Tong, Qi Liu, Zhaofeng Liu
First submitted to arxiv on: 13 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 Unified Evaluation Process of Retrieval-Augmented Generation (RAG) has been proposed to assess the performance of these hybrid models. The evaluation process, dubbed Auepora, aims to provide a comprehensive overview of the metrics and benchmarks used in RAG systems. By examining quantifiable metrics such as relevance, accuracy, and faithfulness within current benchmarks, researchers can better understand the strengths and limitations of existing RAG systems. This study also analyzes various datasets and metrics, highlighting the need for more robust evaluation frameworks to advance the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RAG models are trying to improve language generation by using information from the internet. To see if they’re working well, scientists need a way to test them. Auepora is a new method that helps evaluate these RAG models. It looks at how well they do on different tasks and with different types of data. The goal is to create a better system for testing RAG models so researchers can make progress in this area. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
