@inproceedings{dong-etal-2025-threshold,
title = "Threshold Filtering Packing for Supervised Fine-Tuning: Training Related Samples within Packs",
author = "Dong, Jiancheng and
Jiang, Lei and
Jin, Wei and
Cheng, Lu",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.226/",
doi = "10.18653/v1/2025.naacl-long.226",
pages = "4422--4435",
ISBN = "979-8-89176-189-6",
abstract = "Packing for Supervised Fine-Tuning (SFT) in autoregressive models involves concatenating data points of varying lengths until reaching the designed maximum length to facilitate GPU processing. However, randomly concatenating data points can lead to cross-contamination of sequences due to the significant difference in their subject matter. The mainstream approaches in SFT ensure that each token in the attention calculation phase only focuses on tokens within its own short sequence, without providing additional learning signals for the preceding context. To address these challenges, we introduce Threshold Filtering Packing (TFP), a method that selects samples with related context while maintaining sufficient diversity within the same pack. Our experiments show that TFP offers a simple-to-implement and scalable approach that significantly enhances SFT performance, with observed improvements of up to 7{\%} on GSM8K, 4{\%} on HumanEval. Furthermore, results from bias benchmark datasets highlight TFP{'}s promising performance in improving fairness while also boosting prediction accuracy by 15{\%}."
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<abstract>Packing for Supervised Fine-Tuning (SFT) in autoregressive models involves concatenating data points of varying lengths until reaching the designed maximum length to facilitate GPU processing. However, randomly concatenating data points can lead to cross-contamination of sequences due to the significant difference in their subject matter. The mainstream approaches in SFT ensure that each token in the attention calculation phase only focuses on tokens within its own short sequence, without providing additional learning signals for the preceding context. To address these challenges, we introduce Threshold Filtering Packing (TFP), a method that selects samples with related context while maintaining sufficient diversity within the same pack. Our experiments show that TFP offers a simple-to-implement and scalable approach that significantly enhances SFT performance, with observed improvements of up to 7% on GSM8K, 4% on HumanEval. Furthermore, results from bias benchmark datasets highlight TFP’s promising performance in improving fairness while also boosting prediction accuracy by 15%.</abstract>
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%0 Conference Proceedings
%T Threshold Filtering Packing for Supervised Fine-Tuning: Training Related Samples within Packs
%A Dong, Jiancheng
%A Jiang, Lei
%A Jin, Wei
%A Cheng, Lu
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F dong-etal-2025-threshold
%X Packing for Supervised Fine-Tuning (SFT) in autoregressive models involves concatenating data points of varying lengths until reaching the designed maximum length to facilitate GPU processing. However, randomly concatenating data points can lead to cross-contamination of sequences due to the significant difference in their subject matter. The mainstream approaches in SFT ensure that each token in the attention calculation phase only focuses on tokens within its own short sequence, without providing additional learning signals for the preceding context. To address these challenges, we introduce Threshold Filtering Packing (TFP), a method that selects samples with related context while maintaining sufficient diversity within the same pack. Our experiments show that TFP offers a simple-to-implement and scalable approach that significantly enhances SFT performance, with observed improvements of up to 7% on GSM8K, 4% on HumanEval. Furthermore, results from bias benchmark datasets highlight TFP’s promising performance in improving fairness while also boosting prediction accuracy by 15%.
%R 10.18653/v1/2025.naacl-long.226
%U https://aclanthology.org/2025.naacl-long.226/
%U https://doi.org/10.18653/v1/2025.naacl-long.226
%P 4422-4435
Markdown (Informal)
[Threshold Filtering Packing for Supervised Fine-Tuning: Training Related Samples within Packs](https://aclanthology.org/2025.naacl-long.226/) (Dong et al., NAACL 2025)
ACL