LLM-labeled training sets for entity matching produce student models with F1 scores within 2 points of benchmark-trained models on five datasets at a cost of $28-41 versus 470 hours of manual work.
C o A nnotating: Uncertainty-guided work allocation between human and large language models for data annotation
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
WildFeedback extracts preference pairs from in-situ user feedback in LLM conversations to fine-tune models for better alignment with real user preferences.
Presents a hierarchical two-layer coding scheme integrating cognitive/non-cognitive problem-solving with metacognitive regulation, claiming it discriminates deeper collaboration when applied across nine datasets from multiple domains.
Decomposing annotation tasks using centers from centering theory reduces aggregate inferential load via a degrees-of-freedom model and enables better sub-task allocation.
citing papers explorer
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Labeling Training Data for Entity Matching Using Large Language Models
LLM-labeled training sets for entity matching produce student models with F1 scores within 2 points of benchmark-trained models on five datasets at a cost of $28-41 versus 470 hours of manual work.
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WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback
WildFeedback extracts preference pairs from in-situ user feedback in LLM conversations to fine-tune models for better alignment with real user preferences.
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Bridging Talk and Thought: Understanding Dialogue Dynamics Across Collaborative Problem-Solving Contexts
Presents a hierarchical two-layer coding scheme integrating cognitive/non-cognitive problem-solving with metacognitive regulation, claiming it discriminates deeper collaboration when applied across nine datasets from multiple domains.
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Task Decomposition for Efficient Annotation
Decomposing annotation tasks using centers from centering theory reduces aggregate inferential load via a degrees-of-freedom model and enables better sub-task allocation.