WildLong: Synthesizing Realistic Long-Context Instruction Data at Scale

• URL: http://arxiv.org/abs/2502.16684v1
• Date: Sun, 23 Feb 2025 18:59:09 GMT
• Title: WildLong: Synthesizing Realistic Long-Context Instruction Data at Scale
• Authors: Jiaxi Li, Xingxing Zhang, Xun Wang, Xiaolong Huang, Li Dong, Liang Wang, Si-Qing Chen, Wei Lu, Furu Wei,
• Abstract summary: WildLong extracts meta-information from real user queries to produce scalable data.<n>It supports multi-document reasoning, such as cross-document comparison and aggregation.<n>It surpasses existing open-source long-context-optimized models across benchmarks.
• Score: 86.25450054683172
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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