| ARTEMIS-DA |
- Accuracy (Test)
- 80.8
|
ARTEMIS-DA: An Advanced Reasoning and Transformation Engine for Multi-Step Insight Synthesis in Data Analytics
2024-12-18 |
연결 없음 |
| TabLaP |
- Accuracy (Dev)
- /
- Accuracy (Test)
- 76.6
|
Accurate and Regret-aware Numerical Problem Solver for Tabular Question Answering
2024-10-10 |
코드 1 |
| SynTQA (GPT) |
- Accuracy (Test)
- 74.4
- Accuracy
- 65.2
|
SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQA
2024-09-25 |
코드 1 |
| Mix SC |
- Accuracy (Dev)
- /
- Accuracy (Test)
- 73.6
|
Rethinking Tabular Data Understanding with Large Language Models
2023-12-27 |
코드 1 |
| SynTQA (RF) |
- Accuracy (Dev)
- /
- Accuracy (Test)
- 71.6
|
SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQA
2024-09-25 |
코드 1 |
| CABINET |
- Accuracy (Dev)
- /
- Accuracy (Test)
- 69.1
|
CABINET: Content Relevance based Noise Reduction for Table Question Answering
2024-02-02 |
코드 1 |
| NormTab+TabSQLify |
- Accuracy (Test)
- 68.63
|
NormTab: Improving Symbolic Reasoning in LLMs Through Tabular Data Normalization
2024-06-25 |
코드 1 |
| Chain-of-Table |
- Accuracy (Dev)
- /
- Accuracy (Test)
- 67.31
|
Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding
2024-01-09 |
코드 1 · 코드 2 |
| Tab-PoT |
- Accuracy (Dev)
- /
- Accuracy (Test)
- 66.78
|
Efficient Prompting for LLM-based Generative Internet of Things
2024-06-14 |
연결 없음 |
| Dater |
- Accuracy (Dev)
- 64.8
- Accuracy (Test)
- 65.9
|
Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning
2023-01-31 |
코드 1 · 코드 2 |
| LEVER |
- Accuracy (Dev)
- 64.6
- Accuracy (Test)
- 65.8
|
LEVER: Learning to Verify Language-to-Code Generation with Execution
2023-02-16 |
코드 1 |
| TabSQLify (col+row) |
- Accuracy (Test)
- 64.7
|
TabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table Decomposition
2024-04-15 |
코드 1 · 코드 2 |
| Binder |
- Accuracy (Dev)
- 65.0
- Accuracy (Test)
- 64.6
|
Binding Language Models in Symbolic Languages
2022-10-06 |
코드 1 · 코드 2 · 코드 3 · 코드 4 |
| OmniTab-Large |
- Accuracy (Dev)
- 62.5
- Accuracy (Test)
- 63.3
|
OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering
2022-07-08 |
코드 1 |
| NormTab (Targeted) + SQL |
- Accuracy (Test)
- 61.20
|
NormTab: Improving Symbolic Reasoning in LLMs Through Tabular Data Normalization
2024-06-25 |
코드 1 |
| ReasTAP-Large |
- Accuracy (Dev)
- 59.7
- Accuracy (Test)
- 58.7
|
ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples
2022-10-22 |
코드 1 |
| TAPEX-Large |
- Accuracy (Dev)
- 57.0
- Accuracy (Test)
- 57.5
|
TAPEX: Table Pre-training via Learning a Neural SQL Executor
2021-07-16 |
코드 1 · 코드 2 · 코드 3 · 코드 4 |
| MAPO + TABERTLarge (K = 3) |
- Accuracy (Dev)
- 52.2
- Accuracy (Test)
- 51.8
|
TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data
2020-05-17 |
코드 1 |
| T5-3b(UnifiedSKG) |
- Accuracy (Dev)
- 50.65
- Accuracy (Test)
- 49.29
|
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
2022-01-16 |
코드 1 |
| TAPAS-Large (pre-trained on SQA) |
- Accuracy (Dev)
- /
- Accuracy (Test)
- 48.8
|
TAPAS: Weakly Supervised Table Parsing via Pre-training
2020-04-05 |
코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6 · 코드 7 · 코드 8 |
| Structured Attention |
- Accuracy (Dev)
- 43.7
- Accuracy (Test)
- 44.5
|
Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programs
2019-09-09 |
코드 1 |
| SynTQA (Oracle) |
- Test Accuracy
- 77.5
|
SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQA
2024-09-25 |
코드 1 |