TrustUQA: A Trustful Framework for Unified Structured Data Question Answering
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917867774017536 |
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| author | Zhang, Wen Jin, Long Zhu, Yushan Chen, Jiaoyan Huang, Zhiwei Wang, Junjie Hua, Yin Liang, Lei Chen, Huajun |
| author_facet | Zhang, Wen Jin, Long Zhu, Yushan Chen, Jiaoyan Huang, Zhiwei Wang, Junjie Hua, Yin Liang, Lei Chen, Huajun |
| contents | Natural language question answering (QA) over structured data sources such as tables and knowledge graphs have been widely investigated, especially with Large Language Models (LLMs) in recent years. The main solutions include question to formal query parsing and retrieval-based answer generation. However, current methods of the former often suffer from weak generalization, failing to dealing with multi-types of sources, while the later is limited in trustfulness. In this paper, we propose TrustUQA, a trustful QA framework that can simultaneously support multiple types of structured data in a unified way. To this end, it adopts an LLM-friendly and unified knowledge representation method called Condition Graph(CG), and uses an LLM and demonstration-based two-level method for CG querying. For enhancement, it is also equipped with dynamic demonstration retrieval. We have evaluated TrustUQA with 5 benchmarks covering 3 types of structured data. It outperforms 2 existing unified structured data QA methods. In comparison with the baselines that are specific to one data type, it achieves state-of-the-art on 2 of the datasets. Further more, we have demonstrated the potential of our method for more general QA tasks, QA over mixed structured data and QA across structured data. The code is available at https://github.com/zjukg/TrustUQA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_18916 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TrustUQA: A Trustful Framework for Unified Structured Data Question Answering Zhang, Wen Jin, Long Zhu, Yushan Chen, Jiaoyan Huang, Zhiwei Wang, Junjie Hua, Yin Liang, Lei Chen, Huajun Computation and Language Artificial Intelligence Natural language question answering (QA) over structured data sources such as tables and knowledge graphs have been widely investigated, especially with Large Language Models (LLMs) in recent years. The main solutions include question to formal query parsing and retrieval-based answer generation. However, current methods of the former often suffer from weak generalization, failing to dealing with multi-types of sources, while the later is limited in trustfulness. In this paper, we propose TrustUQA, a trustful QA framework that can simultaneously support multiple types of structured data in a unified way. To this end, it adopts an LLM-friendly and unified knowledge representation method called Condition Graph(CG), and uses an LLM and demonstration-based two-level method for CG querying. For enhancement, it is also equipped with dynamic demonstration retrieval. We have evaluated TrustUQA with 5 benchmarks covering 3 types of structured data. It outperforms 2 existing unified structured data QA methods. In comparison with the baselines that are specific to one data type, it achieves state-of-the-art on 2 of the datasets. Further more, we have demonstrated the potential of our method for more general QA tasks, QA over mixed structured data and QA across structured data. The code is available at https://github.com/zjukg/TrustUQA. |
| title | TrustUQA: A Trustful Framework for Unified Structured Data Question Answering |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2406.18916 |