Summary of Unioqa: a Unified Framework For Knowledge Graph Question Answering with Large Language Models, by Zhuoyang Li et al.
UniOQA: A Unified Framework for Knowledge Graph Question Answering with Large Language Models
by Zhuoyang Li, Liran Deng, Hui Liu, Qiaoqiao Liu, Junzhao Du
First submitted to arxiv on: 4 Jun 2024
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 The paper introduces UniOQA, a unified framework that integrates two parallel workflows for question answering over the OwnThink knowledge graph. The approach harnesses large language models (LLMs) for precise question answering and incorporates a direct-answer-prediction process as a cost-effective complement. To bolster representation capacity, the LLM is fine-tuned to translate questions into Cypher query language (CQL), tackling issues associated with restricted semantic understanding and hallucinations. The Entity and Relation Replacement algorithm ensures executability of the generated CQL, while the Retrieval-Augmented Generation (RAG) process augments overall accuracy in question answering. Experimental findings show UniOQA achieving state-of-the-art results on the benchmark, advancing SpCQL Logical Accuracy to 21.2% and Execution Accuracy to 54.9%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary UniOQA is a new way to answer questions using OwnThink, a big database of Chinese knowledge. Right now, answering questions with OwnThink is not very good because existing methods have some limitations. UniOQA fixes these problems by using two different approaches together. First, it fine-tunes a special language model to translate questions into a format that OwnThink can understand. Then, it uses a process called Retrieval-Augmented Generation (RAG) to get the correct answer from OwnThink. |
Keywords
» Artificial intelligence » Knowledge graph » Language model » Question answering » Rag » Retrieval augmented generation
