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        <title>RECOST: External Knowledge Guided Data-efficient Instruction Tuning</title>
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    <abstract>In the current landscape of large language models (LLMs), the process of instruction tuning serves as an essential step. Considering the high computing power overhead, data-efficient instruction tuning was proposed to reduce the training data size in this process, aiming at selecting high-quality instructional data. Nevertheless, we argue that most current data-efficient instruction-tuning methods are highly dependent on the quality of the original instruction-tuning dataset. When it comes to datasets synthesized by LLMs, a common scenario in this field, dirty samples will even be selected with a higher probability than other samples. To address these challenges, we utilized external knowledge (relevant examples or paragraphs) to evaluate those samples synthesized by LLMs with an in-context-based relative predictive entropy. Based on the new metric, we proposed a framework, dubbed as RECOST, which integrates external-knowledge-base re-ranking and diversity-consistent sampling into a single pipeline. Through extensive experiments on several synthetic datasets (Alpaca and Alpaca-gpt4), we demonstrate the effectiveness of our method and achieve even better results with only 1% of the full dataset.</abstract>
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%0 Conference Proceedings
%T RECOST: External Knowledge Guided Data-efficient Instruction Tuning
%A Zhang, Qi
%A Zhang, Yiming
%A Wang, Haobo
%A Zhao, Junbo
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F zhang-etal-2024-recost
%X In the current landscape of large language models (LLMs), the process of instruction tuning serves as an essential step. Considering the high computing power overhead, data-efficient instruction tuning was proposed to reduce the training data size in this process, aiming at selecting high-quality instructional data. Nevertheless, we argue that most current data-efficient instruction-tuning methods are highly dependent on the quality of the original instruction-tuning dataset. When it comes to datasets synthesized by LLMs, a common scenario in this field, dirty samples will even be selected with a higher probability than other samples. To address these challenges, we utilized external knowledge (relevant examples or paragraphs) to evaluate those samples synthesized by LLMs with an in-context-based relative predictive entropy. Based on the new metric, we proposed a framework, dubbed as RECOST, which integrates external-knowledge-base re-ranking and diversity-consistent sampling into a single pipeline. Through extensive experiments on several synthetic datasets (Alpaca and Alpaca-gpt4), we demonstrate the effectiveness of our method and achieve even better results with only 1% of the full dataset.
%U https://aclanthology.org/2024.findings-acl.648
%P 10911-10921

Markdown (Informal)

[RECOST: External Knowledge Guided Data-efficient Instruction Tuning](https://aclanthology.org/2024.findings-acl.648) (Zhang et al., Findings 2024)

• RECOST: External Knowledge Guided Data-efficient Instruction Tuning (Zhang et al., Findings 2024)

ACL

• Qi Zhang, Yiming Zhang, Haobo Wang, and Junbo Zhao. 2024. RECOST: External Knowledge Guided Data-efficient Instruction Tuning. In Findings of the Association for Computational Linguistics ACL 2024, pages 10911–10921, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.