@inproceedings{li-etal-2024-superfiltering,
title = "Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning",
author = "Li, Ming and
Zhang, Yong and
He, Shwai and
Li, Zhitao and
Zhao, Hongyu and
Wang, Jianzong and
Cheng, Ning and
Zhou, Tianyi",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.769",
doi = "10.18653/v1/2024.acl-long.769",
pages = "14255--14273",
abstract = "Instruction tuning is critical to improve LLMs but usually suffers from low-quality and redundant data. Data filtering for instruction tuning has proved important in improving both the efficiency and performance of the tuning process. But it also leads to extra cost and computation due to the involvement of LLMs in this process. To reduce the filtering cost, we study Superfiltering: Can we use a smaller and weaker model to select data for finetuning a larger and stronger model? Despite the performance gap between weak and strong language models, we find their highly consistent capability to perceive instruction difficulty and data selection results. This enables us to use a much smaller and more efficient model to filter the instruction data used to train a larger language model. Not only does it largely speed up the data filtering, but the filtered-data-finetuned LLM achieves even better performance on standard benchmarks. Extensive experiments validate the efficacy and efficiency of our approach.",
}
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<abstract>Instruction tuning is critical to improve LLMs but usually suffers from low-quality and redundant data. Data filtering for instruction tuning has proved important in improving both the efficiency and performance of the tuning process. But it also leads to extra cost and computation due to the involvement of LLMs in this process. To reduce the filtering cost, we study Superfiltering: Can we use a smaller and weaker model to select data for finetuning a larger and stronger model? Despite the performance gap between weak and strong language models, we find their highly consistent capability to perceive instruction difficulty and data selection results. This enables us to use a much smaller and more efficient model to filter the instruction data used to train a larger language model. Not only does it largely speed up the data filtering, but the filtered-data-finetuned LLM achieves even better performance on standard benchmarks. Extensive experiments validate the efficacy and efficiency of our approach.</abstract>
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%0 Conference Proceedings
%T Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning
%A Li, Ming
%A Zhang, Yong
%A He, Shwai
%A Li, Zhitao
%A Zhao, Hongyu
%A Wang, Jianzong
%A Cheng, Ning
%A Zhou, Tianyi
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F li-etal-2024-superfiltering
%X Instruction tuning is critical to improve LLMs but usually suffers from low-quality and redundant data. Data filtering for instruction tuning has proved important in improving both the efficiency and performance of the tuning process. But it also leads to extra cost and computation due to the involvement of LLMs in this process. To reduce the filtering cost, we study Superfiltering: Can we use a smaller and weaker model to select data for finetuning a larger and stronger model? Despite the performance gap between weak and strong language models, we find their highly consistent capability to perceive instruction difficulty and data selection results. This enables us to use a much smaller and more efficient model to filter the instruction data used to train a larger language model. Not only does it largely speed up the data filtering, but the filtered-data-finetuned LLM achieves even better performance on standard benchmarks. Extensive experiments validate the efficacy and efficiency of our approach.
%R 10.18653/v1/2024.acl-long.769
%U https://aclanthology.org/2024.acl-long.769
%U https://doi.org/10.18653/v1/2024.acl-long.769
%P 14255-14273
Markdown (Informal)
[Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning](https://aclanthology.org/2024.acl-long.769) (Li et al., ACL 2024)
ACL
- Ming Li, Yong Zhang, Shwai He, Zhitao Li, Hongyu Zhao, Jianzong Wang, Ning Cheng, and Tianyi Zhou. 2024. Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 14255–14273, Bangkok, Thailand. Association for Computational Linguistics.