Meta Unveils Llama 3.1 405B for Advanced Synthetic Data Generation

Meta has announced the release of Llama 3.1 405B, their most powerful open large language model (LLM) to date. This model is designed to enhance the generation of synthetic data, a crucial element for fine-tuning foundation LLMs across a variety of industries, including finance, retail, telecom, and healthcare, according to the NVIDIA Technical Blog. [source]

LLM-powered synthetic data for generative AI

With the advent of large language models, the motivation and techniques for generating synthetic data have been significantly improved. Enterprises are leveraging Llama 3.1 405B to fine-tune foundation LLMs for specific use cases such as improving risk assessment in finance, optimizing supply chains in retail, enhancing customer service in telecom, and advancing patient care in healthcare.

Using LLM-generated synthetic data to improve language models

There are two main approaches for generating synthetic data for tuning models: knowledge distillation and self-improvement. Knowledge distillation involves translating the capabilities of a larger model into a smaller model, while self-improvement uses the same model to critique its own reasoning. Both methods can be utilized with Llama 3.1 405B to improve smaller LLMs.

Training an LLM involves three steps: pretraining, fine-tuning, and alignment. Pretraining uses a large corpus of information to teach the model the general structure of a language. Fine-tuning then adjusts the model to follow specific instructions, such as improving logical reasoning or code generation. Finally, alignment ensures that the LLM's responses meet user expectations in terms of style and tone.

Using LLM-generated synthetic data to improve other models and systems

The application of synthetic data extends beyond LLMs to adjacent models and LLM-powered pipelines. For example, retrieval-augmented generation (RAG) uses both an embedding model to retrieve relevant information and an LLM to generate answers. LLMs can be used to parse documents and synthesize data for evaluating and fine-tuning embedding models.

Synthetic data to evaluate RAG

To illustrate the use of synthetic data, consider a pipeline for generating evaluation data for retrieval. This involves generating diverse questions based on different user personas and filtering these questions to ensure relevance and diversity. Finally, the questions are rewritten to match the writing styles of the personas.

For example, a financial analyst might be interested in the financial performance of companies involved in a merger, while a legal expert might focus on regulatory scrutiny. By generating questions tailored to these perspectives, the synthetic data can be used to evaluate retrieval pipelines effectively.

Takeaways

Synthetic data generation is essential for enterprises to develop domain-specific generative AI applications. The Llama 3.1 405B model, paired with NVIDIA Nemotron-4 340B reward model, facilitates the creation of high-quality synthetic data, enabling the development of accurate, custom models.

RAG pipelines are crucial for generating grounded responses based on up-to-date information. The described synthetic data generation workflow helps in evaluating these pipelines, ensuring their accuracy and effectiveness.