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SOTA
语义解析
Semantic Parsing On Wikitablequestions
Semantic Parsing On Wikitablequestions
评估指标
Accuracy (Dev)
Accuracy (Test)
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
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
-
0 of 21 row(s) selected.
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