WikiTableQuestions

Overview edit

Variants: WikiTableQuestions

Associated Benchmarks

This dataset is used in 2 benchmarks:

• Question Answering - Metrics: Accuracy, Accuracy (Test)
• Semantic Parsing - Metrics: Accuracy (Test), Accuracy (Dev), Accuracy, Test Accuracy

Recent Benchmark Submissions

Research Papers

Recent papers with results on this dataset:

• ARTEMIS-DA: An Advanced Reasoning and Transformation Engine for Multi-Step Insight Synthesis in Data Analytics (2024) -
• Accurate and Regret-aware Numerical Problem Solver for Tabular Question Answering (2024) -
• SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQA (2024) -
• NormTab: Improving Symbolic Reasoning in LLMs Through Tabular Data Normalization (2024) -
• Efficient Prompting for LLM-based Generative Internet of Things (2024) -
• TabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table Decomposition (2024) -
• LAPDoc: Layout-Aware Prompting for Documents (2024) -
• CABINET: Content Relevance based Noise Reduction for Table Question Answering (2024) -
• Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding (2024) -
• Rethinking Tabular Data Understanding with Large Language Models (2023) -
• LEVER: Learning to Verify Language-to-Code Generation with Execution (2023) -
• Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning (2023) -
• ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples (2022) -
• Binding Language Models in Symbolic Languages (2022) -
• OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering (2022) -
• UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models (2022) -
• TAPEX: Table Pre-training via Learning a Neural SQL Executor (2021) -
• TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data (2020) -
• TAPAS: Weakly Supervised Table Parsing via Pre-training (2020) -
• Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programs (2019) -