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Found 3 papers, 1 papers with code
Auto-Demo Prompting: Leveraging Generated Outputs as Demonstrations for Enhanced Batch Prompting
Batch prompting is a common technique in large language models (LLMs) used to process multiple inputs simultaneously, aiming to improve computational efficiency.
Cost-Aware Uncertainty Reduction in Schema Matching with GPT-4: The Prompt-Matcher Framework
We introduce a novel framework, Prompt-Matcher, to reduce the uncertainty in the process of integration of multiple automatic schema matching algorithms and the selection of complex parameterization.
On Leveraging Large Language Models for Enhancing Entity Resolution: A Cost-efficient Approach
Entity resolution, the task of identifying and merging records that refer to the same real-world entity, is crucial in sectors like e-commerce, healthcare, and law enforcement.
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