TACO is a task-aware LLM framework with abbreviation expansion, description generation, and task-based revision steps that improves downstream tabular NLP performance by up to 32%.
arXiv preprint arXiv:2104.01785 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
TabEmb decouples LLM-based semantic column embeddings from graph-based structural modeling to produce joint representations that improve table annotation tasks.
citing papers explorer
-
TACO: Task-Aware Column Description Generation Using LLMs
TACO is a task-aware LLM framework with abbreviation expansion, description generation, and task-based revision steps that improves downstream tabular NLP performance by up to 32%.
-
TabEmb: Joint Semantic-Structure Embedding for Table Annotation
TabEmb decouples LLM-based semantic column embeddings from graph-based structural modeling to produce joint representations that improve table annotation tasks.