LongAttn: Selecting Long-context Training Data via Token-level Attention

• URL: http://arxiv.org/abs/2502.16860v2
• Date: Thu, 27 Feb 2025 14:50:10 GMT
• Title: LongAttn: Selecting Long-context Training Data via Token-level Attention
• Authors: Longyun Wu, Dawei Zhu, Guangxiang Zhao, Zhuocheng Yu, Junfeng Ran, Xiangyu Wong, Lin Sun, Sujian Li,
• Abstract summary: LongAttn is a token-level framework to measure the long-range dependencies for the data.<n>We filter LongABC-32K from open-source long-context datasets (ArXiv, Book, and Code)
• Score: 16.30530770590871
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the development of large language models (LLMs), there has been an increasing need for significant advancements in handling long contexts. To enhance long-context capabilities, constructing high-quality training data with long-range dependencies is crucial. Existing methods to select long-context data often rely on sentence-level analysis, which can be greatly optimized in both performance and efficiency. In this paper, we propose a novel token-level framework, LongAttn, which leverages the self-attention mechanism of LLMs to measure the long-range dependencies for the data. By calculating token-level dependency strength and distribution uniformity of token scores, LongAttn effectively quantifies long-range dependencies, enabling more accurate and efficient data selection. We filter LongABC-32K from open-source long-context datasets (ArXiv, Book, and Code). Through our comprehensive experiments, LongAttn has demonstrated its excellent effectiveness, scalability, and efficiency. To facilitate future research in long-context data, we released our code and the high-quality long-context training data LongABC-32K.

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