Semantic Parsing On Wikitablequestions

評価指標

Accuracy (Dev)

Accuracy (Test)

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

			Paper Title
ARTEMIS-DA	-	80.8	ARTEMIS-DA: An Advanced Reasoning and Transformation Engine for Multi-Step Insight Synthesis in Data Analytics
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
Mix SC	/	73.6	Rethinking Tabular Data Understanding with Large Language Models
SynTQA (RF)	/	71.6	SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQA
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
Tab-PoT	/	66.78	Efficient Prompting for LLM-based Generative Internet of Things
Dater	64.8	65.9	Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning
LEVER	64.6	65.8	LEVER: Learning to Verify Language-to-Code Generation with Execution
TabSQLify (col+row)	-	64.7	TabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table Decomposition
Binder	65.0	64.6	Binding Language Models in Symbolic Languages
OmniTab-Large	62.5	63.3	OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering
NormTab (Targeted) + SQL	-	61.20	NormTab: Improving Symbolic Reasoning in LLMs Through Tabular Data Normalization
ReasTAP-Large	59.7	58.7	ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples
TAPEX-Large	57.0	57.5	TAPEX: Table Pre-training via Learning a Neural SQL Executor
MAPO + TABERTLarge (K = 3)	52.2	51.8	TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data
T5-3b(UnifiedSKG)	50.65	49.29	UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
TAPAS-Large (pre-trained on SQA)	/	48.8	TAPAS: Weakly Supervised Table Parsing via Pre-training
Structured Attention	43.7	44.5	Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programs

0 of 21 row(s) selected.

Command Palette

Semantic Parsing On Wikitablequestions

評価指標

評価結果