Large language models (LLMs) with extended context windows enable tasks requiring extensive information integration but are limited by the scarcity of high-quality, diverse datasets for long-context instruction tuning. Existing data synthesis methods focus narrowly on objectives like fact retrieval and summarization, restricting their generalizability to complex, real-world tasks. WildLong extracts meta-information from real user queries, models co-occurrence relationships via graph-based methods, and employs adaptive generation to produce scalable data. It extends beyond single-document tasks to support multi-document reasoning, such as cross-document comparison and aggregation. Our models, finetuned on 150K instruction-response pairs synthesized using WildLong, surpasses existing open-source long-context-optimized models across benchmarks while maintaining strong performance on short-context tasks without incorporating supplementary short-context data. By generating a more diverse and realistic long-context instruction dataset, WildLong enhances LLMs' ability to generalize to complex, real-world reasoning over long contexts, establishing a new paradigm for long-context data synthesis.
View on arXiv@article{li2025_2502.16684,
title={ WildLong: Synthesizing Realistic Long-Context Instruction Data at Scale },
author={ Jiaxi Li and Xingxing Zhang and Xun Wang and Xiaolong Huang and Li Dong and Liang Wang and Si-Qing Chen and Wei Lu and Furu Wei },
journal={arXiv preprint arXiv:2502.16684},
year={ 2025 }
}