mlsanityData to 7 Oct 2026

TrustUQA: A Trustful Framework for Unified Structured Data Question Answering

arXiv 2406.18916 ↗First posted 27 Jun 2024cs.CL cs.AIcited by 12

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Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 37 of its 70 references that are in the map. First posted on arXiv 27 Jun 2024.

Builds on

37 of 70 references.

References · 37

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25 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 6; 2026: 5.

12 papers.

  1. 2025Diagnosing and Addressing Pitfalls in KG-RAG Datasets: Toward More Reliable Benchmarkingcited by 11
  2. 2025FailureSensorIQ: A Multi-Choice QA Dataset for Understanding Sensor Relationships and Failure Modescited by 6
  3. 2024Reasoning Factual Knowledge in Structured Data with Large Language Modelscited by 3
  4. 2025Pandora: A Code-Driven Large Language Model Agent for Unified Reasoning Across Diverse Structured Knowledgecited by 3
  5. 2025Privacy-protected Retrieval-Augmented Generation for Knowledge Graph Question Answeringcited by 2
  6. 2025Self-Correction Distillation for Structured Data Question Answeringcited by 2
  7. 2025Pandora: Leveraging Code-driven Knowledge Transfer for Unified Structured Knowledge Reasoningcited by 1
  8. 2026CRAFTQA: A Code-Driven Adaptive Framework for Complex Structured Data Reasoningcited by 0
  9. 2026Improving Answer Extraction in Context-based Question Answering Systems Using LLMscited by 0
  10. 2026SQuaD-SQL: Efficient Text-to-SQL with Small Language Models via LLM-Guided Knowledge Distillationcited by 0
  11. 2026Evolving from Lessons: Skill-Augmented Table Graph Reasoning for Operation-wise Table Question Answeringcited by 0
  12. 2026DynaContext: Self-Improving Dynamic Contextualization of Optimized Prompts for Heterogeneous Parameter Extractioncited by 0