Transformer-based Large Language Models (LLMs) are pioneering advances in many natural language processing tasks, however, their exceptional capabilities are restricted within the preset context window of Transformer. Position Embedding (PE) scaling methods, while effective in extending the context window to a specific length, demonstrate either notable limitations in their extrapolation abilities or sacrificing partial performance within the context window. Length extrapolation methods, although theoretically capable of extending the context window beyond the training sequence length, often underperform in practical long-context applications. To address these challenges, we propose Continuous Length EXtrapolation (CLEX) for LLMs. We generalise the PE scaling approaches to model the continuous dynamics by ordinary differential equations over the length scaling factor, thereby overcoming the constraints of current PE scaling methods designed for specific lengths. Moreover, by extending the dynamics to desired context lengths beyond the training sequence length, CLEX facilitates the length extrapolation with impressive performance in practical tasks. We demonstrate that CLEX can be seamlessly incorporated into LLMs equipped with Rotary Position Embedding, such as LLaMA and GPT-NeoX, with negligible impact on training and inference latency. Experimental results reveal that CLEX can effectively extend the context window to over 4x or almost 8x training length, with no deterioration in performance. Furthermore, when evaluated on the practical LongBench benchmark, our model trained on a 4k length exhibits competitive performance against state-of-the-art open-source models trained on context lengths up to 32k. Our code is available at https://github.com/DAMO-NLP-SG/CLEX.

翻译：基于Transformer的大型语言模型（LLMs）在众多自然语言处理任务中取得了突破性进展，但其卓越能力受限于Transformer预设的上下文窗口。位置嵌入（PE）缩放方法虽能有效将上下文窗口扩展至特定长度，却存在明显的外推能力局限或需牺牲部分上下文窗口内的性能。长度外推方法虽然在理论上可将上下文窗口扩展至训练序列长度之外，但在实际长上下文应用中常表现不佳。为解决这些问题，我们提出面向大型语言模型的连续长度外推方法（CLEX）。该方法将PE缩放方法泛化为通过长度缩放因子的常微分方程建模连续动态过程，从而突破现有PE缩放方法针对特定长度设计的局限。通过将动态过程扩展至训练序列长度以外的目标上下文长度，CLEX能在实际任务中实现卓越的外推性能。实验表明，CLEX可无缝集成至配备旋转位置嵌入（如LLaMA和GPT-NeoX）的大型语言模型中，且对训练和推理延迟影响极小。结果显示，CLEX能有效将上下文窗口扩展至训练长度的4倍乃至近8倍，且性能无损。在实用LongBench基准测试中，基于4k长度训练的模型展现出与最先进开源模型（训练上下文长度达32k）相媲美的竞争力。我们的代码已开源至https://github.com/DAMO-NLP-SG/CLEX。