Bootstrap Your Own Context Length

• URL: http://arxiv.org/abs/2412.18860v2
• Date: Wed, 19 Mar 2025 02:46:34 GMT
• Title: Bootstrap Your Own Context Length
• Authors: Liang Wang, Nan Yang, Xingxing Zhang, Xiaolong Huang, Furu Wei,
• Abstract summary: We introduce a bootstrapping approach to train long-context language models by exploiting their short-context capabilities only.<n>The proposed data synthesis workflow requires only a short-context language model, a text retriever, and a document collection.<n>We conduct experiments with the open-source Llama-3 family of models and demonstrate that our method can successfully extend the context length to up to 1M tokens.
• Score: 74.61148597039248
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a bootstrapping approach to train long-context language models by exploiting their short-context capabilities only. Our method utilizes a simple agent workflow to synthesize diverse long-context instruction tuning data, thereby eliminating the necessity for manual data collection and annotation. The proposed data synthesis workflow requires only a short-context language model, a text retriever, and a document collection, all of which are readily accessible within the open-source ecosystem. Subsequently, language models are fine-tuned using the synthesized data to extend their context lengths. In this manner, we effectively transfer the short-context capabilities of language models to long-context scenarios through a bootstrapping process. We conduct experiments with the open-source Llama-3 family of models and demonstrate that our method can successfully extend the context length to up to 1M tokens, achieving superior performance across various benchmarks.

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