NExtLong: Toward Effective Long-Context Training without Long Documents

• URL: http://arxiv.org/abs/2501.12766v1
• Date: Wed, 22 Jan 2025 10:01:54 GMT
• Title: NExtLong: Toward Effective Long-Context Training without Long Documents
• Authors: Chaochen Gao, Xing Wu, Zijia Lin, Debing Zhang, Songlin Hu,
• Abstract summary: We propose NExtLong, a novel framework for long-context data through Negative document Extension. NExtLong decomposes a document into multiple meta-chunks and extends the context by interleaving hard negative distractors retrieved from pretraining corpora. Extensive experiments demonstrate that NExtLong achieves significant performance improvements compared to existing long-context synthesis approaches.
• Score: 28.002824369635768
• License:
• Abstract: Large language models (LLMs) with extended context windows have made significant strides yet remain a challenge due to the scarcity of long documents. Existing methods tend to synthesize long-context data but lack a clear mechanism to reinforce the long-range dependency modeling. To address this limitation, we propose NExtLong, a novel framework for synthesizing long-context data through Negative document Extension. NExtLong decomposes a document into multiple meta-chunks and extends the context by interleaving hard negative distractors retrieved from pretraining corpora. This approach compels the model to discriminate long-range dependent context from distracting content, enhancing its ability to model long-range dependencies. Extensive experiments demonstrate that NExtLong achieves significant performance improvements on the HELMET and RULER benchmarks compared to existing long-context synthesis approaches and leading models, which are trained on non-synthetic long documents. These findings highlight NExtLong's ability to reduce reliance on non-synthetic long documents, making it an effective framework for developing advanced long-context LLMs.

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