AutoTQA: Towards Autonomous Tabular Question Answering through Multi-Agent Large Language Models
Summary: AutoTQA: multi-agent LLM framework for multi-table tabular QA across heterogeneous systems, decomposing queries into Planner→Engineer→Executor with Critic/User agents and agent-scheduling. Provides LinguFlow low-code builder and connectors; outperforms prior single-table TQA on four datasets. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jun-Peng Zhu
- 2. Peng Cai
- 3. Kai Xu
- 4. Li Li
- 5. Yishen Sun
- 6. Shuai Zhou
- 7. Haihuang Su
- 8. Liu Tang
- 9. Qi Liu
Incoming Citations (Sorted by Pagerank)
Showing 9 of 9 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 486 | TiDB: A Raft-based HTAP Database | 2020 | VLDB | 0.00022039625 |
| 1,866 | ReAcTable: Enhancing ReAct for Table Question Answering | 2024 | VLDB | 0.00010265592 |
| 1,953 | D-Bot: Database Diagnosis System using Large Language Models | 2024 | VLDB | 9.9701097e-05 |
| 2,693 | Greenplum: A Hybrid Database for Transactional and Analytical Workloads | 2021 | SIGMOD | 8.2845883e-05 |
| 4,327 | Big Metadata: When Metadata is Big Data | 2021 | VLDB | 6.2765351e-05 |
| 5,506 | Can Large Language Models Predict Data Correlations from Column Names? | 2023 | VLDB | 5.4711611e-05 |
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