xlang-ai/icl-selective-annotation
[ICLR 2023] Code for our paper "Selective Annotation Makes Language Models Better Few-Shot Learners"
This project helps natural language processing (NLP) practitioners create high-quality datasets for new tasks more efficiently. It takes unlabeled text data and, using a novel selective annotation method, identifies the most impactful examples for human annotators to label. The result is a smaller, more effective dataset that teaches large language models new tasks with significantly less annotation effort.
109 stars. No commits in the last 6 months.
Use this if you need to quickly adapt large language models to new text-based classification, reasoning, or generation tasks and want to drastically reduce the cost and time spent on data annotation.
Not ideal if you already have a fully labeled, extensive dataset for your task or if your task doesn't involve natural language processing.
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Jul 15, 2023
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