Augmented LLM

Augmented Large Language Models (LLMs) enhance the capabilities of LLMs by integrating external data sources, aiming to improve accuracy, reasoning, and applicability to real-world tasks. Current research focuses on effective data integration methods, such as retrieval-augmented generation (RAG) and fine-tuning, exploring optimal strategies for diverse query types and data formats, including structured data like tables and knowledge bases. This field is significant because it addresses limitations of LLMs in handling complex tasks and diverse data, leading to improved performance in domains like finance, healthcare, and software engineering.