FIESTA uses bandit algorithms to adaptively decide how many seeds and splits to run for each candidate model, focusing effort on promising ones while providing guarantees on selecting the optimal model.
Neural Architectures for Named Entity Recognition
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The hybrid method with LLM-augmented data achieves F1 improvements of 7-24 points over baselines on five Vietnamese domain datasets.
MODEE is a multimodal system that integrates graphs with LLM embeddings to outperform prior open-domain event extraction methods on large datasets.
TabEmb decouples LLM-based semantic column embeddings from graph-based structural modeling to produce joint representations that improve table annotation tasks.
citing papers explorer
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FIESTA: Fast IdEntification of State-of-The-Art models using adaptive bandit algorithms
FIESTA uses bandit algorithms to adaptively decide how many seeds and splits to run for each candidate model, focusing effort on promising ones while providing guarantees on selecting the optimal model.
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A Hybrid Method for Low-Resource Named Entity Recognition
The hybrid method with LLM-augmented data achieves F1 improvements of 7-24 points over baselines on five Vietnamese domain datasets.
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A Multimodal Text- and Graph-Based Approach for Open-Domain Event Extraction from Documents
MODEE is a multimodal system that integrates graphs with LLM embeddings to outperform prior open-domain event extraction methods on large datasets.
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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.