Open-RAG: Enhanced Retrieval-Augmented Reasoning with Open-Source Large
  Language Models
Authors: name: Shayekh Bin Islam, name: Md Asib Rahman, name: K S M Tozammel Hossain, name: Enamul Hoque, name: Shafiq Joty, name: Md Rizwan Parvez

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. To mitigate this gap, we
introduce a novel framework, Open-RAG, designed to enhance reasoning
capabilities in RAG with open-source LLMs. Our framework transforms an
arbitrary dense LLM into a parameter-efficient sparse mixture of experts (MoE)
model capable of handling complex reasoning tasks, including both single- and
multi-hop queries. Open-RAG uniquely trains the model to navigate challenging
distractors that appear relevant but are misleading. As a result, Open-RAG
leverages latent learning, dynamically selecting relevant experts and
integrating external knowledge effectively for more accurate and contextually
relevant responses. In addition, we propose a hybrid adaptive retrieval method
to determine retrieval necessity and balance the trade-off between performance
gain and inference speed. Experimental results show that the Llama2-7B-based
Open-RAG outperforms state-of-the-art LLMs and RAG models such as ChatGPT,
Self-RAG, and Command R+ in various knowledge-intensive tasks. We open-source
our code and models at https://openragmoe.github.io/