3 papers
cs.CL2024
Auto-Demo Prompting: Leveraging Generated Outputs as Demonstrations for Enhanced Batch Prompting
Longyu Feng, Mengze Hong, Chen Jason Zhang
Batch prompting is a common technique in large language models (LLMs) used to process multiple inputs simultaneously, aiming to improve computational efficiency. However, as batch…
cs.DB2024
Prompt-Matcher: Leveraging Large Models to Reduce Uncertainty in Schema Matching Results
Longyu Feng, Huahang Li, Chen Jason Zhang
Schema matching is the process of identifying correspondences between the elements of two given schemata, essential for database management systems, data integration, and data ware…
cs.CL2024
On Leveraging Large Language Models for Enhancing Entity Resolution: A Cost-efficient Approach
Huahang Li, Longyu Feng, Shuangyin Li +3
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. Lar…