Fact-Centric Knowledge Web for Information Retrieval

The unreliability of Large Language Models (LLMs) owing to Machine Hallucination necessitates a shift towards Retrieval Augmented Generation (RAG), to ensure the veracity of LLM generated content. Inspired by Knowledge Graph based RAG techniques and their advantages of reduced token cost, improved computational performance and knowledge discovery, we present a “Knowledge Web” Structure and its associated techniques for Information Storage and Retrieval. We also explore the structure's synergy with a Fact Finding LLM Agent, capable of generating answers for multi-hop questions from challenging datasets such as the MuSiQue dataset. While still in the experimental phase, our initial results are promising, indicating the potential efficacy of our approach compared to existing RAG techniques.