Semantic Parsing On Wikitablequestions
평가 지표
Accuracy (Dev)
Accuracy (Test)
평가 결과
이 벤치마크에서 각 모델의 성능 결과
비교 표
| 모델 이름 | Accuracy (Dev) | Accuracy (Test) |
|---|---|---|
| efficient-prompting-for-llm-based-generative | / | 66.78 |
| cabinet-content-relevance-based-noise | / | 69.1 |
| chain-of-table-evolving-tables-in-the | / | 67.31 |
| reastap-injecting-table-reasoning-skills | 59.7 | 58.7 |
| tabsqlify-enhancing-reasoning-capabilities-of | - | 64.7 |
| syntqa-synergistic-table-based-question | / | 71.6 |
| binding-language-models-in-symbolic-languages | 65.0 | 64.6 |
| tapas-weakly-supervised-table-parsing-via-pre | / | 48.8 |
| unifiedskg-unifying-and-multi-tasking | 50.65 | 49.29 |
| tabert-pretraining-for-joint-understanding-of | 52.2 | 51.8 |
| normtab-improving-symbolic-reasoning-in-llms | - | 61.20 |
| learning-semantic-parsers-from-denotations | 43.7 | 44.5 |
| large-language-models-are-versatile | 64.8 | 65.9 |
| tapex-table-pre-training-via-learning-a | 57.0 | 57.5 |
| accurate-and-regret-aware-numerical-problem | / | 76.6 |
| syntqa-synergistic-table-based-question | - | 74.4 |
| syntqa-synergistic-table-based-question | - | - |
| advanced-reasoning-and-transformation-engine | - | 80.8 |
| lever-learning-to-verify-language-to-code | 64.6 | 65.8 |
| omnitab-pretraining-with-natural-and-1 | 62.5 | 63.3 |
| rethinking-tabular-data-understanding-with | / | 73.6 |