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SOTA
Semantische Analyse
Semantic Parsing On Wikitablequestions
Semantic Parsing On Wikitablequestions
Metriken
Accuracy (Dev)
Accuracy (Test)
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
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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