From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866911828716552192 |
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| author | Li, Ming Zhang, Yong Li, Zhitao Chen, Jiuhai Chen, Lichang Cheng, Ning Wang, Jianzong Zhou, Tianyi Xiao, Jing |
| author_facet | Li, Ming Zhang, Yong Li, Zhitao Chen, Jiuhai Chen, Lichang Cheng, Ning Wang, Jianzong Zhou, Tianyi Xiao, Jing |
| contents | In the realm of Large Language Models (LLMs), the balance between instruction data quality and quantity is a focal point. Recognizing this, we introduce a self-guided methodology for LLMs to autonomously discern and select cherry samples from open-source datasets, effectively minimizing manual curation and potential cost for instruction tuning an LLM. Our key innovation, the Instruction-Following Difficulty (IFD) metric, emerges as a pivotal metric to identify discrepancies between a model's expected responses and its intrinsic generation capability. Through the application of IFD, cherry samples can be pinpointed, leading to a marked uptick in model training efficiency. Empirical validations on datasets like Alpaca and WizardLM underpin our findings; with a mere $10\%$ of original data input, our strategy showcases improved results. This synthesis of self-guided cherry-picking and the IFD metric signifies a transformative leap in the instruction tuning of LLMs, promising both efficiency and resource-conscious advancements. Codes, data, and models are available: https://github.com/tianyi-lab/Cherry_LLM |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_12032 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning Li, Ming Zhang, Yong Li, Zhitao Chen, Jiuhai Chen, Lichang Cheng, Ning Wang, Jianzong Zhou, Tianyi Xiao, Jing Computation and Language In the realm of Large Language Models (LLMs), the balance between instruction data quality and quantity is a focal point. Recognizing this, we introduce a self-guided methodology for LLMs to autonomously discern and select cherry samples from open-source datasets, effectively minimizing manual curation and potential cost for instruction tuning an LLM. Our key innovation, the Instruction-Following Difficulty (IFD) metric, emerges as a pivotal metric to identify discrepancies between a model's expected responses and its intrinsic generation capability. Through the application of IFD, cherry samples can be pinpointed, leading to a marked uptick in model training efficiency. Empirical validations on datasets like Alpaca and WizardLM underpin our findings; with a mere $10\%$ of original data input, our strategy showcases improved results. This synthesis of self-guided cherry-picking and the IFD metric signifies a transformative leap in the instruction tuning of LLMs, promising both efficiency and resource-conscious advancements. Codes, data, and models are available: https://github.com/tianyi-lab/Cherry_LLM |
| title | From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2308.12032 |