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Supervised finetuning (SFT) on instruction datasets has played a crucial role in achieving the remarkable zero-shot generalization capabilities observed in modern large language models (LLMs).
Deep batch active learning by diverse, uncertain gradient lower bounds
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Zeroprompt: Scaling prompt-based pretraining to 1,000 tasks improves zero-shot generalization
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Data diversity matters for robust instruction tuning
Alexander Bukharin and Tuo Zhao. 2023 · 2023
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Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, and Sham Kakade. 2021 · 2021
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The flan collection: Designing data and methods for effective instruction tuning
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Cit: Curation in training for effective vision-language data
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