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näkemys - Instruction Tuning with LLMs
SELECTLLM: Effective Selection of Unlabeled Instructions for Annotation by LLMs
LLMs can effectively select important unlabelled instructions for annotation, improving instruction tuning benchmarks.
SELECTLLM: Leveraging LLMs for Efficient Instruction Selection
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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