Kavli Affiliate: Gang Su | First 5 Authors: Huifeng Lin, Huifeng Lin, , , | Summary: Retrieval-Augmented Generation (RAG) based on Large Language Models (LLMs) is a powerful solution to understand and query the industry’s closed-source documents. However, basic RAG often struggles with complex QA tasks in legal and regulatory domains, particularly when dealing with […]
Continue.. Fishing for Answers: Exploring One-shot vs. Iterative Retrieval Strategies for Retrieval Augmented Generation