This blog post discusses the use of knowledge graphs and LLMs in multi-hop question answering. It highlights the limitations of simple vector similarity search and the need for retrieving information from multiple documents. The post explores the concept of retrieval-augmented approach and how it enhances the capability of LLMs to generate accurate answers. It also introduces the idea of using knowledge graphs as condensed information storage and the benefits it offers in terms of query efficiency and multi-hop reasoning. The post concludes by emphasizing the importance of leveraging knowledge graphs in retrieval-augmented generation applications for improved query performance and support for structured and unstructured information.
| Signal | Change | 10y horizon | Driving force |
|---|---|---|---|
| Knowledge Graphs & LLMs: Multi-Hop Question Answering | Retrieving information across multiple documents | Improved ability to generate accurate and relevant answers | Enhancing the capability of Large Language Models (LLMs) |
| Using Knowledge Graphs in Chain-of-Thought Flow | Combining structured and unstructured data for reasoning | Improved query efficiency and multi-hop reasoning capabilities | Incorporating knowledge graphs into LLM applications |
| Combining Graph and Textual Data | Integration of textual and graph data for information retrieval | More accurate and relevant information retrieval | Enhancing retrieval-augmented generation applications |
| Knowledge Graph as Condensed Information Storage | Condensing information for easy access during query time | Reduced noise and better results | Improving latency and avoiding runtime issues in retrieval-augmented applications |