learn.ifs.name

Self-Instruct: Aligning Language Models with Self-Generated Instructions

Wang et al., 2023, Self-Instruct (Proceedings of the 61st Annual Meeting of ACL (Volume 1: Long Papers))

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4.5 Effect of Data Size and Quality

  1. How did the amount and quality of Self-Instruct data affect results?
    Human-rated quality improved with more data but nearly plateaued after about 16K instructions (on SuperNI already after hundreds). Regenerating the outputs with InstructGPT-003 improved the fine-tuned model by 10%, showing much room for better-quality data.
    • Wang et al., 2023, Self-Instruct (Proceedings of the 61st Annual Meeting of ACL (Volume 1: Long Papers)), §4.5, p. 13491

Source

  • Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi and Hannaneh Hajishirzi. 2023. Self-Instruct: Aligning Language Models with Self-Generated Instructions. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 13484–13508. License: CC BY 4.0. Flashcards adapted from this work. doi:10.18653/v1/2023.acl-long.754 https://aclanthology.org/2023.acl-long.754/