Summary of Keqing: Knowledge-based Question Answering Is a Nature Chain-of-thought Mentor Of Llm, by Chaojie Wang et al.
keqing: knowledge-based question answering is a nature chain-of-thought mentor of LLM
by Chaojie Wang, Yishi Xu, Zhong Peng, Chenxi Zhang, Bo Chen, Xinrun Wang, Lei Feng, Bo An
First submitted to arxiv on: 31 Dec 2023
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel framework, called Knowledge-based question answering (Keqing), is proposed to assist large language models (LLMs) like ChatGPT in retrieving structured information from knowledge graphs and generating answers to complex questions. Keqing achieves this by decomposing a complex question into sub-questions, retrieving candidate entities on the knowledge graph, reasoning answers to these sub-questions, and then generating a response with reasoning paths. This approach improves the reliability of LLM responses and is demonstrated to achieve competitive performance on KBQA datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are really good at answering questions, but sometimes they get stuck when faced with tricky problems outside their knowledge area. To help them, scientists came up with an idea called Keqing. It’s a way for these models to find answers by looking at a special kind of graph that contains lots of information. First, Keqing breaks down a hard question into smaller ones, then it searches the graph for relevant answers, and finally it puts all those answers together in a logical order. This makes the model’s responses much more reliable and accurate. |
Keywords
» Artificial intelligence » Knowledge graph » Question answering
