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.

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。位置编码（PE）缩放方法虽能有效将上下文窗口扩展至特定长度，却在扩展能力上存在显著局限，或在上下文窗口内牺牲部分性能。长度外推方法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往往表现不佳。为应对这些挑战，我们提出面向大型语言模型的连续长度外推方法（CLEX）。通过将位置编码缩放方法泛化为关于长度缩放因子的常微分方程连续动态建模，我们突破了当前针对特定长度设计的PE缩放方法的约束。此外，通过将动态扩展至训练序列长度之外的期望上下文长度，CLEX在实际任务中实现了令人瞩目的长度外推性能。我们证明，CLEX可无缝集成到配备旋转位置编码的LLMs（如LLaMA和GPT-NeoX）中，且对训练和推理延迟的影响可忽略不计。实验结果表明，CLEX能有效将上下文窗口扩展至训练长度的4倍甚至近8倍，且性能无衰减。此外，在实际LongBench基准测试中，我们基于4k长度训练的模型在性能上与现有最先进的、在高达32k上下文长度上训练的开源模型展现出竞争力。