Context-level Language Modeling by Learning Predictive Context Embeddings

• URL: http://arxiv.org/abs/2510.20280v2
• Date: Tue, 28 Oct 2025 07:35:34 GMT
• Title: Context-level Language Modeling by Learning Predictive Context Embeddings
• Authors: Beiya Dai, Yuliang Liu, Daozheng Xue, Qipeng Guo, Kai Chen, Xinbing Wang, Bowen Zhou, Zhouhan Lin,
• Abstract summary: We introduce textbfContextLM, a framework that augments standard pretraining with an inherent textbfnext-context prediction objective.<n>This mechanism trains the model to learn predictive representations of multi-token contexts, leveraging error signals derived from future token chunks.<n>Experiments on the GPT2 and Pythia model families, scaled up to $1.5$B parameters, show that ContextLM delivers consistent improvements in both perplexity and downstream task performance.
• Score: 79.00607069677393
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Next-token prediction (NTP) is the cornerstone of modern large language models (LLMs) pretraining, driving their unprecedented capabilities in text generation, reasoning, and instruction following. However, the token-level prediction limits the model's capacity to capture higher-level semantic structures and long-range contextual relationships. To overcome this limitation, we introduce \textbf{ContextLM}, a framework that augments standard pretraining with an inherent \textbf{next-context prediction} objective. This mechanism trains the model to learn predictive representations of multi-token contexts, leveraging error signals derived from future token chunks. Crucially, ContextLM achieves this enhancement while remaining fully compatible with the standard autoregressive, token-by-token evaluation paradigm (e.g., perplexity). Extensive experiments on the GPT2 and Pythia model families, scaled up to $1.5$B parameters, show that ContextLM delivers consistent improvements in both perplexity and downstream task performance. Our analysis indicates that next-context prediction provides a scalable and efficient pathway to stronger language modeling, yielding better long-range coherence and more effective attention allocation with minimal computational overhead.

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