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ReAcTable: Enhancing ReAct for Table Question Answering
Summary: ReAcTable adapts the ReAct paradigm to TableQA, combining incremental reasoning with external SQL/Python executors to generate intermediate table representations that resolve complex semantics and noisy/inconsistent data. Evaluated on three TQA benchmarks, it yields SOTA-level results, improving WikiTQ to 68.0% (+2.1%) without fine-tuning.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 13433
- Venue
- VLDB
- Year
- 2024
- Pagerank
- 0.00010259702
- Overall Rank
- 1,872 | 86.98%
- DOI
-
10.14778/3659437.3659452
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 16 of 16 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 3,114 |
GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization |
2024 |
VLDB |
7.5451724e-05 |
| 4,739 |
AutoTQA: Towards Autonomous Tabular Question Answering through Multi-Agent Large Language Models |
2024 |
VLDB |
5.959592e-05 |
| 6,217 |
Pneuma: Leveraging LLMs for Tabular Data Representation and Retrieval in an End-to-End System |
2025 |
SIGMOD |
5.1534752e-05 |
| 7,139 |
Automated Validating and Fixing of Text-to-SQL Translation with Execution Consistency |
2025 |
SIGMOD |
4.821174e-05 |
| 9,032 |
Sphinteract: Resolving Ambiguities in NL2SQL Through User Interaction |
2025 |
VLDB |
4.4039656e-05 |
| 9,991 |
The Pneuma Project: Reifying Information Needs as Relational Schemas to Automate Discovery, Guide Preparation, and Align Data with Intent |
2026 |
CIDR |
4.1945683e-05 |
| 10,115 |
ST-Raptor: LLM-Powered Semi-Structured Table Question Answering |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,117 |
AixelAsk: A Stepwise-Guided Retrieval and Reasoning Framework for Large Table QA |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,143 |
Beluga: A CXL-Based Memory Architecture for Scalable and Efficient LLM KVCache Management |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,185 |
MultiVis-Agent: A Multi-Agent Framework with Logic Rules for Reliable and Comprehensive Cross-Modal Data Visualization |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,217 |
This is Going to Sound Crazy, But What If We Used Large Language Models to Boost Automatic Database Tuning Algorithms By Leveraging Prior History? We Will Find Better Configurations More Quickly Than Retraining From Scratch! |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,460 |
UNITQA: A Unified Automated Tabular Question Answering System with Multi-Agent Large Language Models |
2025 |
SIGMOD |
4.1945683e-05 |
| 10,589 |
Birdie: Natural Language-Driven Table Discovery Using Differentiable Search Index |
2025 |
VLDB |
4.1945683e-05 |
| 10,595 |
Optimized Batch Prompting for Cost-effective LLMs |
2025 |
VLDB |
4.1945683e-05 |
| 10,682 |
AutoPrep: Natural Language Question-Aware Data Preparation with a Multi-Agent Framework |
2025 |
VLDB |
4.1945683e-05 |
| 10,753 |
Cents: A Flexible and Cost-Effective Framework for LLM-Based Table Understanding |
2025 |
VLDB |
4.1945683e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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AutoTQA: Towards Autonomous Tabular Question Answering through Multi-Agent Large Language Models |
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