Summary of Similarity Is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts, by Chunjing Gan et al.
Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts
by Chunjing Gan, Dan Yang, Binbin Hu, Hanxiao Zhang, Siyuan Li, Ziqi Liu, Yue Shen, Lin Ju, Zhiqiang Zhang, Jinjie Gu, Lei Liang, Jun Zhou
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 novel framework, MetRag, to enhance retrieval augmented generation (RAG) in knowledge-intensive tasks. The authors argue that similarity-based methods can be insufficient and propose a multi-layered approach that combines utility-oriented thought and compactness-oriented thought. The framework consists of three stages: retrieving documents using a small-scale utility model, adapting an LLM as a task adaptive summarizer to capture commonalities among retrieved documents, and finally, augmenting knowledge with the outputs from the previous stages. Experimental results on various tasks demonstrate the superiority of MetRag. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how we can use computers to get better at learning and remembering new information. Right now, big language models (LLMs) are really good at doing certain things, but they have some problems too, like taking a long time to update their knowledge and sometimes making mistakes. The authors of this paper came up with a new way to improve LLMs by combining different ideas together. They call it MetRag, which stands for Multi layEred Thoughts enhanced Retrieval Augmented Generation. It’s kind of like having a team of experts working together to help us learn and remember better. |
Keywords
* Artificial intelligence * Rag * Retrieval augmented generation
