The Large Language Model Market is expected to grow rapidly in the coming years. To give you an idea, the market is projected to increase from $6.4 billion in 2024 to $36.1 billion by 2030. This means it will expand at an annual growth rate of 33.2% between 2024 and 2030. Several factors are driving this expansion including the growing need for seamless interaction between humans and machines and the escalating demand for automated content creation. One of the most popular LLMs GPT-3 is trained on massive amounts of text data from the web. This allows the model to understand and generate human-like text on a wide range of topics.
However, sometimes you might want LLMs to learn specific knowledge not included in their original training data. This could be the latest news, industry information or proprietary data. A technique called RAG (Retrieval-Augmented Generation) allows LLMs to retrieve and use this new knowledge while generating outputs.
But how can you optimise the RAG process for peak performance? A recent research published by Cornell University focuses on techniques to fine-tune or additionally train LLMs on the new knowledge domain. One such approach is called RAFT (Retrieval Augmented Fine-Tuning). With RAFT, the LLM learns to identify the most relevant information from retrieved documents to comprehensively answer a given question. It cites verbatim from the right document sections while ignoring irrelevant "distractor" content.
Also Read: Running a Chatbot: How-to Guide
The RAG (Retrieval-Augmented Generation) model is a cutting-edge approach used to improve the existing capabilities of LLMs by integrating external knowledge retrieval mechanisms. The core architecture of RAG models involves two key components: a retriever and a generator.
But how exactly do RAG models leverage retrievers to retrieve relevant context before generation? When presented with a query, the retriever first searches for the knowledge source to identify and fetch relevant context. This retrieved context is then fed into the generator, which uses its language understanding and generation capabilities to produce a response that incorporates the external knowledge naturally and coherently.
For instance, if asked a query about training generative AI for 3D models, the retriever might locate relevant research papers or tutorials on machine learning techniques for 3D model generation. It would prioritise sourcing information on popular frameworks like Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs) adapted specifically for 3D modelling tasks. Hence, with this contextual understanding, the generator will present an informed and detailed response, drawing upon the retrieved context.
While RAG models have shown great potential in leveraging external knowledge for language generation tasks, it does have their own set of challenges. Some of them include:
Fine-tuning large language models on domain-specific data and tasks came out as a strong technique to address these challenges. Traditional fine-tuning methods involve updating the model's parametres on a domain-specific dataset to adapt the model's knowledge and capabilities to the target domain. While this approach did show promising results, we now have advanced fine-tuning techniques to tackle the modern challenges and requirements of RAG models such as:
After employing the fine-tuning techniques to large language models, it is equally important to evaluate the performance of RAG models to identify effectiveness and areas for improvement. Some evaluation metrics commonly used to assess different aspects of these models' performance include:
The process of fine-tuning large language models is computationally intensive requiring high performance computing resources. This is where GPU comes into play, they expedite the fine-tuning process and enable efficient training of RAG models. LLMs involve complex neural network architectures with millions or even billions of parameters, and the computations required for fine-tuning these models can be parallelised across the numerous cores available on modern GPUs.
When looking for GPU options suitable for fine-tuning LLMs for RAG models, several factors need to be considered, including compute power, memory capacity and budget. High-performance GPUs like the NVIDIA A100 and NVIDIA H100 offer exceptional computing power and memory capacity, making them well-suited for fine-tuning large and complex LLMs.
It's worth noting that GPU acceleration is not limited to the fine-tuning process alone. It can also be leveraged during the inference stage, where RAG models are deployed for tasks like question answering, summarisation, or information retrieval. By utilising GPU acceleration during inference, these models can deliver faster and more efficient responses, crucial for real-time or time-sensitive applications.
Fine-tuning large language models is imperative to explore the full potential of RAG (Retrieval-Augmented Generation) models for knowledge-intensive applications. By adapting these powerful models to specific domains and tasks, you can improve retrieval accuracy, generation coherence, and ranking precision, ultimately delivering more informed and context-aware outputs.
As the field of natural language processing continues to grow, it is important to stay informed of the latest advancements in fine-tuning techniques. Advanced strategies like data augmentation, domain-specific pre-training, multi-task learning and reinforcement learning approaches offer exciting opportunities to push the boundaries of RAG model performance further.
Sign up today to experience the power of high-end NVIDIA GPUs for LLMs!
The retriever is responsible for identifying and fetching relevant contextual information from an external knowledge source like a document corpus or knowledge base. It provides the language model with background knowledge to produce more informed and context-aware outputs. Common retrieval techniques include sparse vector indexing and dense passage retrieval.
Fine-tuning helps address challenges like retrieval quality, generation coherence, and ranking accuracy in RAG models. It adapts the pre-trained language model to the target domain, improving its ability to leverage retrieved-context effectively.
RAFT is a fine-tuning technique where the language model is trained to simultaneously identify relevant passages and generate responses citing verbatim from the retrieved context. This encourages a better understanding of the retrieval-generation interplay, improving performance on open-book question-answering tasks.
Key evaluation metrics include Recall@K and MRR for retrieval accuracy, Perplexity and BLEU/ROUGE for generation fluency, and Precision@K for ranking precision.