Semantic Parsing On Wikitablequestions

Metrics

Accuracy (Dev)

Accuracy (Test)

Results

Performance results of various models on this benchmark

Model Name	Accuracy (Dev)	Accuracy (Test)	Paper Title	Repository
Tab-PoT	/	66.78	Efficient Prompting for LLM-based Generative Internet of Things	-
CABINET	/	69.1	CABINET: Content Relevance based Noise Reduction for Table Question Answering	-
Chain-of-Table	/	67.31	Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding	-
ReasTAP-Large	59.7	58.7	ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples	-
TabSQLify (col+row)	-	64.7	TabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table Decomposition	-
SynTQA (RF)	/	71.6	SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQA	-
Binder	65.0	64.6	Binding Language Models in Symbolic Languages	-
TAPAS-Large (pre-trained on SQA)	/	48.8	TAPAS: Weakly Supervised Table Parsing via Pre-training	-
T5-3b(UnifiedSKG)	50.65	49.29	UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models	-
MAPO + TABERTLarge (K = 3)	52.2	51.8	TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data	-
NormTab (Targeted) + SQL	-	61.20	NormTab: Improving Symbolic Reasoning in LLMs Through Tabular Data Normalization	-
Structured Attention	43.7	44.5	Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programs	-
Dater	64.8	65.9	Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning	-
TAPEX-Large	57.0	57.5	TAPEX: Table Pre-training via Learning a Neural SQL Executor	-
TabLaP	/	76.6	Accurate and Regret-aware Numerical Problem Solver for Tabular Question Answering	-
SynTQA (GPT)	-	74.4	SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQA	-
SynTQA (Oracle)	-	-	SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQA	-
ARTEMIS-DA	-	80.8	ARTEMIS-DA: An Advanced Reasoning and Transformation Engine for Multi-Step Insight Synthesis in Data Analytics	-
LEVER	64.6	65.8	LEVER: Learning to Verify Language-to-Code Generation with Execution	-
OmniTab-Large	62.5	63.3	OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering	-

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