@inproceedings{chen-etal-2024-long,
title = "Long Context is Not Long at All: A Prospector of Long-Dependency Data for Large Language Models",
author = "Chen, Longze and
Liu, Ziqiang and
He, Wanwei and
Zheng, Yinhe and
Sun, Hao and
Li, Yunshui and
Luo, Run and
Yang, Min",
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.447",
pages = "8222--8234",
abstract = "Long-context modeling capabilities are important for large language models (LLMs) in various applications. However, directly training LLMs with long context windows is insufficient to enhance this capability since some training samples do not exhibit strong semantic dependencies across long contexts.In this study, we propose a data mining framework ProLong that can assign each training sample with a long dependency score, which can be used to rank and filter samples that are more advantageous for enhancing long-context modeling abilities in LLM training. Specifically, we first use delta perplexity scores to measure the Dependency Strength between text segments in a given document. Then, we refine this metric based on the Dependency Distance of these segments to incorporate spatial relationships across long contexts. Final results are calibrated with a Dependency Specificity metric to prevent trivial dependencies introduced by repetitive patterns. Moreover, a random sampling approach is proposed to optimize the computational efficiency of ProLong. Comprehensive experiments on multiple benchmarks indicate that ProLong effectively identifies documents that carry long dependencies, and LLMs trained on these documents exhibit significantly enhanced long-context modeling capabilities.",
}
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<abstract>Long-context modeling capabilities are important for large language models (LLMs) in various applications. However, directly training LLMs with long context windows is insufficient to enhance this capability since some training samples do not exhibit strong semantic dependencies across long contexts.In this study, we propose a data mining framework ProLong that can assign each training sample with a long dependency score, which can be used to rank and filter samples that are more advantageous for enhancing long-context modeling abilities in LLM training. Specifically, we first use delta perplexity scores to measure the Dependency Strength between text segments in a given document. Then, we refine this metric based on the Dependency Distance of these segments to incorporate spatial relationships across long contexts. Final results are calibrated with a Dependency Specificity metric to prevent trivial dependencies introduced by repetitive patterns. Moreover, a random sampling approach is proposed to optimize the computational efficiency of ProLong. Comprehensive experiments on multiple benchmarks indicate that ProLong effectively identifies documents that carry long dependencies, and LLMs trained on these documents exhibit significantly enhanced long-context modeling capabilities.</abstract>
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%0 Conference Proceedings
%T Long Context is Not Long at All: A Prospector of Long-Dependency Data for Large Language Models
%A Chen, Longze
%A Liu, Ziqiang
%A He, Wanwei
%A Zheng, Yinhe
%A Sun, Hao
%A Li, Yunshui
%A Luo, Run
%A Yang, Min
%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 chen-etal-2024-long
%X Long-context modeling capabilities are important for large language models (LLMs) in various applications. However, directly training LLMs with long context windows is insufficient to enhance this capability since some training samples do not exhibit strong semantic dependencies across long contexts.In this study, we propose a data mining framework ProLong that can assign each training sample with a long dependency score, which can be used to rank and filter samples that are more advantageous for enhancing long-context modeling abilities in LLM training. Specifically, we first use delta perplexity scores to measure the Dependency Strength between text segments in a given document. Then, we refine this metric based on the Dependency Distance of these segments to incorporate spatial relationships across long contexts. Final results are calibrated with a Dependency Specificity metric to prevent trivial dependencies introduced by repetitive patterns. Moreover, a random sampling approach is proposed to optimize the computational efficiency of ProLong. Comprehensive experiments on multiple benchmarks indicate that ProLong effectively identifies documents that carry long dependencies, and LLMs trained on these documents exhibit significantly enhanced long-context modeling capabilities.
%U https://aclanthology.org/2024.acl-long.447
%P 8222-8234
Markdown (Informal)
[Long Context is Not Long at All: A Prospector of Long-Dependency Data for Large Language Models](https://aclanthology.org/2024.acl-long.447) (Chen et al., ACL 2024)
ACL
- Longze Chen, Ziqiang Liu, Wanwei He, Yinhe Zheng, Hao Sun, Yunshui Li, Run Luo, and Min Yang. 2024. Long Context is Not Long at All: A Prospector of Long-Dependency Data for Large Language Models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8222–8234, Bangkok, Thailand. Association for Computational Linguistics.