Original Paper: https://arxiv.org/abs/2408.02545
By: Daniel Fleischer, Moshe Berchansky, Moshe Wasserblat, Peter Izsak
Abstract:
Implementing Retrieval-Augmented Generation (RAG) systems is inherently complex, requiring deep understanding of data, use cases, and intricate design decisions. Additionally, evaluating these systems presents significant challenges, necessitating assessment of both retrieval accuracy and generative quality through a multi-faceted approach. We introduce RAG Foundry, an open-source framework for augmenting large language models for RAG use cases. RAG Foundry integrates data creation, training, inference and evaluation into a single workflow, facilitating the creation of data-augmented datasets for training and evaluating large language models in RAG settings. This integration enables rapid prototyping and experimentation with various RAG techniques, allowing users to easily generate datasets and train RAG models using internal or specialized knowledge sources. We demonstrate the framework effectiveness by augmenting and fine-tuning Llama-3 and Phi-3 models with diverse RAG configurations, showcasing consistent improvements across three knowledge-intensive datasets. Code is released as open-source in
In the vast and rapidly evolving landscape of AI, Large Language Models (LLMs) like GPT-3 and Llama-3 have emerged as transformative forces. They demonstrate impressive capabilities across a wide range of tasks traditionally requiring human intelligence. However, these models aren't without limitations—they can produce convincing but incorrect answers, struggle with factual accuracy, and lack up-to-date information post-training. To address these challenges, Retrieval-Augmented Generation (RAG) systems offer a promising solution by integrating external information retrieval mechanisms to enhance the performance of LLMs.
In this blog post, we'll explore RAG Foundry, a robust open-source framework developed by Intel Labs that aims to streamline the implementation of RAG systems. We'll delve into its methodologies, key findings, and the potential implications of this innovative framework.
RAG Foundry is designed to simplify the complex process of integrating retrieval mechanisms with large language models. It provides a comprehensive, end-to-end workflow that includes data creation, training, inference, and evaluation. This modular approach facilitates rapid prototyping and experimentation with various RAG techniques, enabling users to generate datasets and train RAG models using both internal and specialized knowledge sources.
The framework's capabilities are demonstrated through experiments involving the augmentation and fine-tuning of Llama-3 and Phi-3 models, showcasing consistent improvements across three knowledge-intensive datasets.