LongMagpie: A Self-synthesis Method for Generating Large-scale Long-context Instructions

2025-05-22

AI Summary

To address the scarcity of high-quality instruction data, prohibitive human annotation costs, and limited diversity and scalability of template-based synthesis methods in aligning long-context large language models (LLMs), this paper proposes LongMagpie—a self-synthesizing framework. LongMagpie introduces a novel model-autoregressive instruction generation paradigm: leveraging already-aligned long-context LLMs (e.g., Qwen, Llama), it employs prompt engineering, special-token triggering mechanisms, and document-query-response triplet construction to fully automatically synthesize high-quality, diverse, long-context instruction data from raw documents—without human annotation. The framework enables open-source, scalable, and cost-effective dataset construction. Experiments demonstrate that models trained on LongMagpie-generated data achieve state-of-the-art performance on long-context benchmarks—including HELMET, RULER, and LongBench v2—while preserving competitive short-context accuracy.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Computer Vision: Large Vision Models

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

High-quality long-context instruction data is essential for aligning long-context large language models (LLMs). Despite the public release of models like Qwen and Llama, their long-context instruction data remains proprietary. Human annotation is costly and challenging, while template-based synthesis methods limit scale, diversity, and quality. We introduce LongMagpie, a self-synthesis framework that automatically generates large-scale long-context instruction data. Our key insight is that aligned long-context LLMs, when presented with a document followed by special tokens preceding a user turn, auto-regressively generate contextually relevant queries. By harvesting these document-query pairs and the model's responses, LongMagpie produces high-quality instructions without human effort. Experiments on HELMET, RULER, and Longbench v2 demonstrate that LongMagpie achieves leading performance on long-context tasks while maintaining competitive performance on short-context tasks, establishing it as a simple and effective approach for open, diverse, and scalable long-context instruction data synthesis.

Problem

• Generating large-scale long-context instruction data automatically
• Overcoming limitations of human annotation and template-based synthesis
• Enhancing performance in long-context tasks without sacrificing short-context accuracy

Innovation

• Self-synthesis framework for long-context data
• Generates queries using aligned LLMs automatically
• Produces scalable high-quality instructions without humans