
The author introduces SELECTLLM, a framework that utilizes LLMs to select high-quality unlabelled instructions efficiently, outperforming traditional methods in instruction tuning benchmarks.


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selectllm-leveraging-llms-for-efficient-instruction-selection


SELECTLLM: Leveraging LLMs for Efficient Instruction Selection


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LLMs can effectively select important unlabelled instructions for annotation, improving instruction tuning benchmarks.


selectllm-effective-selection-of-unlabeled-instructions-for-annotation-by-llms


SELECTLLM: Effective Selection of Unlabeled Instructions for Annotation by LLMs
