RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation (AI summary)

Key Points

1. The paper introduces RAG Foundry, an open-source framework for augmenting large language models (LLMs) for Retrieval Augmented Generation (RAG) use cases.

2. RAG Foundry integrates data creation, training, inference and evaluation into a single workflow, facilitating rapid prototyping and experimentation with various RAG techniques.

3. The framework allows users to easily generate datasets and train RAG models using internal or specialized knowledge sources.

4. The authors demonstrate the framework's effectiveness by augmenting and fine-tuning LLAMA-3 and Phi-3 models with diverse RAG configurations, showing consistent improvements across three knowledge-intensive datasets.

5. The paper highlights the inherent limitations of LLMs, such as producing incorrect or nonsensical answers, lacking factual accuracy, and struggling with large contexts.

6. RAG enhances LLM performance by integrating external information using retrieval mechanisms, addressing knowledge limitations, reducing hallucinations, and improving the relevance of generated content.

7. Implementing RAG systems is complex, requiring a thorough understanding of data and use cases, as well as making numerous intricate design decisions.

8. Evaluating RAG systems presents challenges due to the need to assess both retrieval accuracy and generative quality through a multi-faceted approach.

9. The paper outlines the modular design of RAG Foundry, including its four distinct modules: data creation, training, inference, and evaluation, which enable flexible and efficient data processing tailored to RAG-oriented tasks.

Summary

This research paper introduces RAG FOUNDRY, an open-source framework for developing and evaluating retrieval-augmented generation (RAG) systems. RAG systems integrate external information sources with large language models (LLMs) to enhance their performance and address their inherent limitations, such as producing inaccurate or nonsensical outputs, lacking up-to-date information, and struggling with relevant information in large contexts. The paper highlights the complexity of implementing and evaluating RAG systems, which require intricate design decisions regarding text embedding, indexing, retrieval algorithms, query building, and prompt design.

The authors note that evaluating RAG systems also presents challenges due to the need to assess both retrieval accuracy and generative quality through a multi-faceted approach. The RAG FOUNDRY framework is designed to facilitate rapid prototyping and experimentation with various RAG techniques. It consists of four integrated modules: data creation, training, inference, and evaluation.

The data creation module enables the generation of context-enhanced datasets by persisting RAG interactions, ensuring compatibility and reproducibility across different models and experiments. The training module leverages the well-established TRL framework to fine-tune LLMs for RAG use cases, while the inference module generates predictions using the processed datasets.

The evaluation module allows for the running of various metrics, including wrappers for existing evaluation libraries, as well as custom metrics such as faithfulness and relevancy, to assess the performance of RAG systems. To demonstrate the utility of the RAG FOUNDRY framework, the authors conduct experiments involving retrieval, fine-tuning, chain-of-thought (CoT) reasoning, and a negative distractor-documents technique.

They compare the performance of two baseline models, Llama-3 and Phi-3, across three knowledge-intensive question-answering tasks: TriviaQA, PubmedQA, and ASQA. The results show that the various RAG augmentation techniques consistently improve performance across the datasets, with the CoT fine-tuning approach performing best in most cases.

The paper highlights the importance of a multi-faceted evaluation approach for RAG systems and the need for a flexible and customizable framework like RAG FOUNDRY to enable rapid prototyping and experimentation in this complex domain. The open-source release of the framework aims to support researchers and practitioners in enhancing LLMs for RAG use cases.

Reference: https://arxiv.org/abs/2408.02545