Large language models (LLMs) excel at various tasks due to their vast training datasets, but their knowledge can be static and lack domain-specific nuance. Researchers have explored methods like fine-tuning and retrieval-augmented generation (RAG) to address these limitations. 

Fine-tuning involves adjusting a pre-trained model on a narrower dataset to enhance its performance in a specific domain. RAG, on the other hand, expands LLMs' capabilities, especially in knowledge-intensive tasks, by using external knowledge sources. 

This episode discusses a research paper comparing fine-tuning and RAG as methods for injecting knowledge into LLMs to improve their accuracy in answering factual questions. The authors evaluated these methods on various knowledge-intensive tasks using popular open-source LLMs (Llama2-7B, Mistral-7B, and Orca2-7B), drawing data from the MMLU benchmark and a custom-created current events dataset. 

Resources:
https://arxiv.org/pdf/2312.05934