LADM: Long-context Training Data Selection with Attention-based Dependency Measurement for LLMs

• URL: http://arxiv.org/abs/2503.02502v1
• Date: Tue, 04 Mar 2025 11:10:13 GMT
• Title: LADM: Long-context Training Data Selection with Attention-based Dependency Measurement for LLMs
• Authors: Jianghao Chen, Junhong Wu, Yangyifan Xu, Jiajun Zhang,
• Abstract summary: Long-context modeling has drawn more and more attention in the area of Large Language Models (LLMs)<n>We propose a Long-context data selection framework with Attention-based Dependency Measurement (LADM)<n>LADM can efficiently identify high-quality long-context data from a large-scale, multi-domain pre-training corpus.
• Score: 8.34562564266839
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long-context modeling has drawn more and more attention in the area of Large Language Models (LLMs). Continual training with long-context data becomes the de-facto method to equip LLMs with the ability to process long inputs. However, it still remains an open challenge to measure the quality of long-context training data. To address this issue, we propose a Long-context data selection framework with Attention-based Dependency Measurement (LADM), which can efficiently identify high-quality long-context data from a large-scale, multi-domain pre-training corpus. LADM leverages the retrieval capabilities of the attention mechanism to capture contextual dependencies, ensuring a comprehensive quality measurement of long-context data. Experimental results show that our LADM framework significantly boosts the performance of LLMs on multiple long-context tasks with only 1B tokens for continual training.

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