Long Context is Not Long at All: A Prospector of Long-Dependency Data for Large Language Models

TL;DR

This paper introduces ProLong, a data mining framework that identifies training samples with strong long-range dependencies to improve large language models' ability to understand long contexts.

Contribution

ProLong is a novel data filtering method that scores and selects samples with meaningful long dependencies, enhancing LLMs' long-context modeling capabilities.

Findings

ProLong effectively identifies documents with strong long dependencies.

LLMs trained on ProLong-selected data show improved long-context understanding.

The framework improves long-context modeling without increasing training complexity.

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 \textbf{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 \textit{Dependency Strength} between text segments in a given document. Then we refine this metric based on the \textit{Dependency Distance} of these segments to incorporate spatial relationships across long-contexts. Final results are calibrated with a \textit{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.