Introduction
Large Language Models (LLMs) have the capability to answer a wide range of questions. However, they may stumble when dealing with topics not in their training data, such as recent events or deep – web information (data not indexed by search engines). Additionally, the lack of a clear answer source makes verification difficult. Retrieval Augmented Generation (RAG) comes to the rescue here. RAG combines the generative power of LLMs with information retrieval from external data sources and can also cite the exact answer sources, enhancing verifiability and reliability. In this article, we’ll explore enhancing RAG through Retrieval Augmented Fine – tuning.
Learning Objectives
Learners will first identify the limitations of LLMs and understand how RAG boosts answer accuracy and reliability by integrating external data. They will also learn the process of preparing data for LLM fine – tuning, including data chunking, question generation, and the selection of oracle and distractor contexts. Additionally, learners will get familiar with configuring the RAFTDatasetPack parameters for optimal question and answer generation from LLMs and comprehend how the Semantic Splitter Node Parser processes data into chunks and the importance of cosine dissimilarity in improving model understanding.
Adapting LLM to RAG
In RAG, data is split into chunks. Then, the top – K most similar chunks to a query are found and presented to the LLM for answer generation. But these chunks may contain both relevant and irrelevant content. If we can fine – tune the LLM to handle this situation, where it has to find relevant content among given chunks to generate an answer, it can enhance the accuracy of RAG.
What is Retrieval Augmented Fine – tuning?
Generating answers based solely on training data is like a “closed – book” exam, while using external data, as in RAG, is like an “open – book” exam. A new method involves training the LLM on how to effectively use external data, which has significantly improved RAG performance.
How to Prepare the Data for Fine – tuning the LLM?
To prepare data for LLM fine – tuning, we first divide sample data into chunks, each potentially being a source of information for question generation. Then, we generate corresponding questions for each data chunk. Answers are generated using the ‘oracle context’ (the relevant data chunk) with Chain of Thought prompting. Alongside, we select ‘distractor contexts’ (random data chunks) to simulate noise. All these elements, including the question, oracle context, distractor contexts, and the generated answer, are compiled into a training dataset, which is then used for fine – tuning the model.
Implementation of Retrieval Augmented Fine – tuning
First, install the required libraries: pip install llama - index and pip install llama - index - packs - raft - dataset. Then, import the RAFTDataset. The RAFTDatasetPack is configured with parameters like filepath (the source file for data), llm (the chosen LLM, with GPT – 4 as default), embed - model (for similarity calculation), num_questions_per_chunk, num_distract_docs, etc. The Semantic Splitter Node Parser splits the data into semantically coherent chunks based on cosine dissimilarity. The implementation steps include importing necessary libraries, loading the OpenAI API key, defining llm and embedding models, downloading a dataset, creating and running the RAFT Dataset object, and finally loading the dataset.
Key Takeaways
RAG significantly improves LLM performance by integrating external data, enabling more accurate and verifiable answers. Fine – tuning LLMs with a well – prepared dataset enhances the model’s ability to distinguish relevant information. The Semantic Splitter Node Parser plays a crucial role in data preprocessing for better model training and performance.
Conclusion
The combination of RAG and LLMs helps mitigate the limitations of LLMs. Fine – tuning with prepared datasets and using preprocessing techniques like the Semantic Splitter Node Parser marks a significant step forward in AI applications, highlighting the need for innovation in AI for more reliable solutions.
Frequently Asked Questions
Q1. What is Retrieval Augmented Generation? A. RAG is a technique that enhances LLMs by using external data sources, allowing for more accurate and up – to – date answers, especially for untrained topics.
Q2. How does fine – tuning improve LLM performance in this example? A. Fine – tuning with a specific dataset improves the LLM’s ability to prioritize relevant information, leading to more precise responses.
Q3. What is the RAFT Dataset, and how does it relate to RAG? A. The RAFT Dataset is designed for fine – tuning LLMs in a RAG setup. It contains a carefully prepared dataset with questions, oracle, and distractor contexts to help the LLM use external data effectively.
