ST-Raptor: LLM-Powered Semi-Structured Table Question Answering
Summary: ST-Raptor: tree-based LLM QA for semi-structured tables that models complex layouts via a Hierarchical Orthogonal Tree (HO-Tree) and decomposes questions into tree-operation pipelines aligned to table structure. Includes forward/backward verification, releases SSTQA dataset, and outperforms nine baselines by up to 20% in accuracy. (summarized by gpt-5-mini on Feb 11 2026)
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Authors
- 1. Zirui Tang
- 2. Boyu Niu
- 3. Xuanhe Zhou
- 4. Boxiu Li
- 5. Wei Zhou
- 6. Jiannan Wang
- 7. Guoliang Li
- 8. Xinyi Zhang
- 9. Fan Wu
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,116 | Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes | 2024 | VLDB | 0.00013890154 |
| 1,872 | ReAcTable: Enhancing ReAct for Table Question Answering | 2024 | VLDB | 0.00010259702 |
| 2,587 | Table-GPT: Table Fine-tuned GPT for Diverse Table Tasks | 2024 | SIGMOD | 8.4924618e-05 |
| 3,859 | OpenSearch-SQL: Enhancing Text-to-SQL with Dynamic Few-shot and Consistency Alignment | 2025 | SIGMOD | 6.6907933e-05 |
| 7,354 | Reliable Text-to-SQL with Adaptive Abstention | 2025 | SIGMOD | 4.7529612e-05 |
| 7,424 | Table Extraction and Understanding for Scientific and Enterprise Applications | 2020 | VLDB | 4.7339251e-05 |
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