Summary of Retrieval Augmented Generation (rag) and Beyond: a Comprehensive Survey on How to Make Your Llms Use External Data More Wisely, by Siyun Zhao et al.
Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely
by Siyun Zhao, Yuqing Yang, Zilong Wang, Zhiyuan He, Luna K. Qiu, Lili Qiu
First submitted to arxiv on: 23 Sep 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 Large language models (LLMs) with external data have shown impressive capabilities in completing real-world tasks. Techniques like Retrieval-Augmented Generation (RAG) and fine-tuning are gaining attention for integrating external data into LLMs, and their widespread application is anticipated. However, deploying data-augmented LLMs across various specialized fields poses significant challenges, including retrieving relevant data, accurately interpreting user intent, and harnessing the reasoning capabilities of LLMs for complex tasks. This survey proposes a RAG task categorization method to identify the core focus of tasks and disentangle multiple capabilities required for better resolution. The four levels of queries include explicit fact queries, implicit fact queries, interpretable rationale queries, and hidden rationale queries, with relevant datasets provided. The survey also highlights key challenges and effective techniques for addressing them, discussing three forms of integrating external data: context, small model, and fine-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to make large language models (LLMs) work better by adding more information from the outside world. Right now, LLMs are very good at doing some things, but they struggle with others. To help them do better, we need to add the right kind of information and understand what users really want. The authors suggest a way to organize tasks into different levels based on how much external data is needed and what kind of information is required. They also discuss three methods for adding external data: using context, training a smaller model, or fine-tuning. Overall, this paper aims to help people build better LLM applications by understanding the challenges and finding solutions. |
Keywords
» Artificial intelligence » Attention » Fine tuning » Rag » Retrieval augmented generation
