One-Shot Learning as Instruction Data Prospector for Large Language Models
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arXiv
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917682460229632 |
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| author | Li, Yunshui Hui, Binyuan Xia, Xiaobo Yang, Jiaxi Yang, Min Zhang, Lei Si, Shuzheng Chen, Ling-Hao Liu, Junhao Liu, Tongliang Huang, Fei Li, Yongbin |
| author_facet | Li, Yunshui Hui, Binyuan Xia, Xiaobo Yang, Jiaxi Yang, Min Zhang, Lei Si, Shuzheng Chen, Ling-Hao Liu, Junhao Liu, Tongliang Huang, Fei Li, Yongbin |
| contents | Contemporary practices in instruction tuning often hinge on enlarging data scaling without a clear strategy for ensuring data quality, inadvertently introducing noise that may compromise model performance. To address this challenge, we introduce \textsc{Nuggets}, a novel and efficient methodology that leverages one-shot learning to discern and select high-quality instruction data from extensive datasets. \textsc{Nuggets} assesses the potential of individual instruction examples to act as effective one-shot learning instances, thereby identifying those that can significantly improve performance across diverse tasks. \textsc{Nuggets} utilizes a scoring system based on the impact of candidate examples on the perplexity of a diverse anchor set, facilitating the selection of the most advantageous data for instruction tuning. Through comprehensive evaluations on two benchmarks, including MT-Bench and Alpaca-Eval, we show that instruction tuning with the top 1\% of examples curated by \textsc{Nuggets} substantially outperforms conventional methods employing the entire dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_10302 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | One-Shot Learning as Instruction Data Prospector for Large Language Models Li, Yunshui Hui, Binyuan Xia, Xiaobo Yang, Jiaxi Yang, Min Zhang, Lei Si, Shuzheng Chen, Ling-Hao Liu, Junhao Liu, Tongliang Huang, Fei Li, Yongbin Computation and Language Artificial Intelligence Contemporary practices in instruction tuning often hinge on enlarging data scaling without a clear strategy for ensuring data quality, inadvertently introducing noise that may compromise model performance. To address this challenge, we introduce \textsc{Nuggets}, a novel and efficient methodology that leverages one-shot learning to discern and select high-quality instruction data from extensive datasets. \textsc{Nuggets} assesses the potential of individual instruction examples to act as effective one-shot learning instances, thereby identifying those that can significantly improve performance across diverse tasks. \textsc{Nuggets} utilizes a scoring system based on the impact of candidate examples on the perplexity of a diverse anchor set, facilitating the selection of the most advantageous data for instruction tuning. Through comprehensive evaluations on two benchmarks, including MT-Bench and Alpaca-Eval, we show that instruction tuning with the top 1\% of examples curated by \textsc{Nuggets} substantially outperforms conventional methods employing the entire dataset. |
| title | One-Shot Learning as Instruction Data Prospector for Large Language Models |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2312.10302 |