Summary of Open-rag: Enhanced Retrieval-augmented Reasoning with Open-source Large Language Models, by Shayekh Bin Islam et al.
Open-RAG: Enhanced Retrieval-Augmented Reasoning with Open-Source Large Language Models
by Shayekh Bin Islam, Md Asib Rahman, K S M Tozammel Hossain, Enamul Hoque, Shafiq Joty, Md Rizwan Parvez
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 Retrieval-Augmented Generation (RAG) has been shown to enhance the factual accuracy of Large Language Models (LLMs), but existing methods often suffer from limited reasoning capabilities in effectively using the retrieved evidence, particularly when using open-source LLMs. Our novel framework, Open-RAG, addresses this gap by transforming an arbitrary dense LLM into a parameter-efficient sparse mixture of experts (MoE) model capable of handling complex reasoning tasks. Open-RAG uniquely trains the model to navigate challenging distractors and leverages latent learning for more accurate and contextually relevant responses. We also propose a hybrid adaptive retrieval method to balance performance gain and inference speed. Experimental results show that our Llama2-7B-based Open-RAG outperforms state-of-the-art LLMs and RAG models in various knowledge-intensive tasks, including single- and multi-hop queries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine using a special tool that helps you find the right answer from lots of information. This tool is called Retrieval-Augmented Generation (RAG). But, existing tools have a problem – they can’t understand complex questions very well. Our new tool, Open-RAG, solves this problem by making it easier for computers to use the information they find. We also made a way to make sure the computer doesn’t get fooled by misleading information. This helped our tool answer questions more accurately and give better responses. We tested our tool on many different types of questions and found that it does much better than other tools. |
Keywords
» Artificial intelligence » Inference » Mixture of experts » Parameter efficient » Rag » Retrieval augmented generation
