TrustUQA: A Trustful Framework for Unified Structured Data Question Answering

• URL: http://arxiv.org/abs/2406.18916v2
• Date: Fri, 13 Dec 2024 15:15:46 GMT
• Title: TrustUQA: A Trustful Framework for Unified Structured Data Question Answering
• Authors: Wen Zhang, Long Jin, Yushan Zhu, Jiaoyan Chen, Zhiwei Huang, Junjie Wang, Yin Hua, Lei Liang, Huajun Chen,
• Abstract summary: We propose TrustUQA, a trustful QA framework that can simultaneously support multiple types of structured data in a unified way. We have evaluated TrustUQA with 5 benchmarks covering 3 types of structured data. In comparison with the baselines that are specific to one data type, it achieves state-of-the-art on 2 of the datasets.
• Score: 45.480862651323115
• License:
• Abstract: 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.

Related papers

• Exploring Hint Generation Approaches in Open-Domain Question Answering [16.434748534272014]
  We introduce a novel context preparation approach called HINTQA. Unlike traditional methods, HINTQA prompts LLMs to produce hints about potential answers for the question. We demonstrate that hints enhance the accuracy of answers more than retrieved and generated contexts.
  arXiv  Detail & Related papers  (2024-09-24T13:50:32Z)
• Prompting Large Language Models with Chain-of-Thought for Few-Shot Knowledge Base Question Generation [19.327008532572645]
  Question Generation over Knowledge Bases (KBQG) aims to convert a logical form into a natural language question. We propose Chain-of-Thought prompting, which is an in-context learning strategy for reasoning. We conduct extensive experiments over three public KBQG datasets.
  arXiv  Detail & Related papers  (2023-10-12T15:08:14Z)
• ADMUS: A Progressive Question Answering Framework Adaptable to Multiple Knowledge Sources [9.484792817869671]
  We present ADMUS, a progressive knowledge base question answering framework designed to accommodate a wide variety of datasets. Our framework supports the seamless integration of new datasets with minimal effort, only requiring creating a dataset-related micro-service at a negligible cost.
  arXiv  Detail & Related papers  (2023-08-09T08:46:39Z)
• An Empirical Comparison of LM-based Question and Answer Generation Methods [79.31199020420827]
  Question and answer generation (QAG) consists of generating a set of question-answer pairs given a context. In this paper, we establish baselines with three different QAG methodologies that leverage sequence-to-sequence language model (LM) fine-tuning. Experiments show that an end-to-end QAG model, which is computationally light at both training and inference times, is generally robust and outperforms other more convoluted approaches.
  arXiv  Detail & Related papers  (2023-05-26T14:59:53Z)
• UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph [89.98762327725112]
  Multi-hop Question Answering over Knowledge Graph(KGQA) aims to find the answer entities that are multiple hops away from the topic entities mentioned in a natural language question. We propose UniKGQA, a novel approach for multi-hop KGQA task, by unifying retrieval and reasoning in both model architecture and parameter learning.
  arXiv  Detail & Related papers  (2022-12-02T04:08:09Z)
• SYGMA: System for Generalizable Modular Question Answering OverKnowledge Bases [57.89642289610301]
  We present SYGMA, a modular approach facilitating general-izability across multiple knowledge bases and multiple rea-soning types. We demonstrate effectiveness of our system by evaluating on datasets belonging to two distinct knowledge bases,DBpedia and Wikidata.
  arXiv  Detail & Related papers  (2021-09-28T01:57:56Z)
• Unified Open-Domain Question Answering with Structured and Unstructured Knowledge [7.7429684536437104]
  We study open-domain question answering (ODQA) with structured, unstructured and semi-structured knowledge sources. Our approach homogenizes all sources by reducing them to text, and applies recent, powerful retriever-reader models. As a result, our unified model produces state-of-the-art results on 3 popular ODQA benchmarks.
  arXiv  Detail & Related papers  (2020-12-29T05:14:08Z)
• Open Question Answering over Tables and Text [55.8412170633547]
  In open question answering (QA), the answer to a question is produced by retrieving and then analyzing documents that might contain answers to the question. Most open QA systems have considered only retrieving information from unstructured text. We present a new large-scale dataset Open Table-and-Text Question Answering (OTT-QA) to evaluate performance on this task.
  arXiv  Detail & Related papers  (2020-10-20T16:48:14Z)
• Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs [62.71505254770827]
  We propose a conditional variational autoencoder (HCVAE) for generating QA pairs given unstructured texts as contexts. Our model obtains impressive performance gains over all baselines on both tasks, using only a fraction of data for training.
  arXiv  Detail & Related papers  (2020-05-28T08:26:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.