@inproceedings{he-etal-2024-softdedup,
title = "{S}oft{D}edup: an Efficient Data Reweighting Method for Speeding Up Language Model Pre-training",
author = "He, Nan and
Xiong, Weichen and
Liu, Hanwen and
Liao, Yi and
Ding, Lei and
Zhang, Kai and
Tang, Guohua and
Han, Xiao and
Wei, Yang",
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.220",
pages = "4011--4022",
abstract = "The effectiveness of large language models (LLMs) is often hindered by duplicated data in their extensive pre-training datasets. Current approaches primarily focus on detecting and removing duplicates, which risks the loss of valuable information and neglects the varying degrees of duplication. To address this, we propose a soft deduplication method that maintains dataset integrity while selectively reducing the sampling weight of data with high commonness. Central to our approach is the concept of {``}data commonness{''}, a metric we introduce to quantify the degree of duplication by measuring the occurrence probabilities of samples using an n-gram model. Empirical analysis shows that this method significantly improves training efficiency, achieving comparable perplexity scores with at least a 26{\%} reduction in required training steps. Additionally, it enhances average few-shot downstream accuracy by 1.77{\%} when trained for an equivalent duration. Importantly, this approach consistently improves performance, even on rigorously deduplicated datasets, indicating its potential to complement existing methods and become a standard pre-training process for LLMs.",
}
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%0 Conference Proceedings
%T SoftDedup: an Efficient Data Reweighting Method for Speeding Up Language Model Pre-training
%A He, Nan
%A Xiong, Weichen
%A Liu, Hanwen
%A Liao, Yi
%A Ding, Lei
%A Zhang, Kai
%A Tang, Guohua
%A Han, Xiao
%A Wei, Yang
%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 he-etal-2024-softdedup
%X The effectiveness of large language models (LLMs) is often hindered by duplicated data in their extensive pre-training datasets. Current approaches primarily focus on detecting and removing duplicates, which risks the loss of valuable information and neglects the varying degrees of duplication. To address this, we propose a soft deduplication method that maintains dataset integrity while selectively reducing the sampling weight of data with high commonness. Central to our approach is the concept of “data commonness”, a metric we introduce to quantify the degree of duplication by measuring the occurrence probabilities of samples using an n-gram model. Empirical analysis shows that this method significantly improves training efficiency, achieving comparable perplexity scores with at least a 26% reduction in required training steps. Additionally, it enhances average few-shot downstream accuracy by 1.77% when trained for an equivalent duration. Importantly, this approach consistently improves performance, even on rigorously deduplicated datasets, indicating its potential to complement existing methods and become a standard pre-training process for LLMs.
%U https://aclanthology.org/2024.acl-long.220
%P 4011-4022
Markdown (Informal)
[SoftDedup: an Efficient Data Reweighting Method for Speeding Up Language Model Pre-training](https://aclanthology.org/2024.acl-long.220) (He et al., ACL 2024)
ACL
- Nan He, Weichen Xiong, Hanwen Liu, Yi Liao, Lei Ding, Kai Zhang, Guohua Tang, Xiao Han, and Yang Wei. 2024. SoftDedup: an Efficient Data Reweighting Method for Speeding Up Language Model Pre-training. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4011–4022, Bangkok, Thailand. Association for Computational Linguistics.