This episode explores the world of data-augmented Large Language Models (LLMs) and their ability to handle increasingly complex real-world tasks. It introduces a four-tiered framework for categorizing user queries based on complexity, showing how data augmentation enhances LLMs' problem-solving capabilities.The episode begins with explicit fact queries (L1), where answers are directly retrieved from external data using techniques like Retrieval-Augmented Generation (RAG). It then moves to implicit fact queries (L2), which require the integration of multiple facts through reasoning, discussing techniques like iterative RAG and Natural Language to SQL queries.For interpretable rationale queries (L3), LLMs must follow explicit reasoning from external sources like manuals or workflows, with strategies like prompt optimization and Chain-of-Thought prompting. Finally, hidden rationale queries (L4) demand extracting implicit reasoning from diverse data, using methods like few-shot learning and fine-tuning to adapt LLMs to complex problems.The episode provides listeners with a comprehensive understanding of how data-augmented LLMs tackle diverse tasks and emphasizes the importance of selecting the right data injection mechanisms for different query types.
https://arxiv.org/pdf/2409.14924v1