Large language models (LLMs) call for extension of context to handle many
critical applications. However, the existing approaches are prone to expensive
costs and inferior quality of context extension. In this work, we propose
Extensible Embedding, which realizes high-quality extension of LLM's context
with strong flexibility and cost-effectiveness. Extensible embedding stand as
an enhancement of typical token embedding, which represents the information for
an extensible scope of context instead of a single token. By leveraging such
compact input units of higher information density, the LLM can access to a vast
scope of context even with a small context window. Extensible embedding is
systematically optimized in architecture and training method, which leads to
multiple advantages. 1) High flexibility of context extension, which flexibly
supports ad-hoc extension of diverse context lengths. 2) Strong sample
efficiency of training, which enables the embedding model to be learned in a
cost-effective way. 3) Superior compatibility with the existing LLMs, where the
extensible embedding can be seamlessly introduced as a plug-in component.
Comprehensive evaluations on long-context language modeling and understanding
tasks verify extensible embedding as an effective, efficient, flexible, and
compatible method to extend the LLM's context.

通过利用高信息密度的紧凑输入单元，可实现对大范围上下文的访问，使得可拓展嵌入成为高效、灵活且兼容的方法来扩展大语言模型的上下文。

可扩展的嵌入：适用于 LLM 上下文长度的灵活乘数

Extensible Embedding: A Flexible Multipler For LLM's Context Length

Large language models (LLMs) call for extension of context to handle many
critical applications. However, the existing approaches are prone to expensive
costs and inferior quality of context extension. In this work, we
proposeExtensible Embedding, which realizes high-quality extension of LLM's
context with strong flexibility and cost-effectiveness. Extensible embedding
stand as an enhancement of typical token embedding, which represents the
information for an extensible scope of context instead of a single token. By
leveraging such compact input units of higher information density, the LLM can
access to a vast scope of context even with a small context window. Extensible
embedding is systematically optimized in architecture and training method,
which leads to multiple advantages. 1) High flexibility of context extension,
which flexibly supports ad-hoc extension of diverse context lengths. 2) Strong
sample efficiency of training, which enables the embedding model to be learned
in a cost-effective way. 3) Superior compatibility with the existing LLMs,
where the extensible embedding can be seamlessly introduced as a plug-in
component. Comprehensive evaluations on long-context language modeling and
understanding tasks verify extensible embedding as an effective, efficient,
flexible, and compatible method to extend the LLM's context.

通过可扩展嵌入实现了高质量、灵活、具有成本效益的扩展大型语言模型的上下文，通过优化架构和训练方法，具有上下文扩展的高灵活性、低成本的训练和与现有大型语言模型的兼容性，进而在长上下文语言建模和理解任务上进行综合评估，验证了可扩展嵌入作为一种有效、高效、灵活和兼容的扩展大型语言模型上下文的方法。