  Many studies have revealed that large language models (LLMs) exhibit uneven
awareness of different contextual positions.Their limited context awareness can
lead to overlooking critical information and subsequent task failures. While
several approaches have been proposed to enhance LLMs' context awareness,
achieving both effectiveness and efficiency remains challenging.In this paper,
for LLMs utilizing RoPE as position embeddings, we introduce a novel method
called ``Mixture of In-Context Experts'' (MoICE) to address this challenge.
MoICE comprises two key components: a router integrated into each attention
head within LLMs and a lightweight router-only training optimization strategy:
(1) MoICE views each RoPE angle as an `in-context' expert, demonstrated to be
capable of directing the attention of a head to specific contextual positions.
Consequently, each attention head flexibly processes tokens using multiple RoPE
angles dynamically selected by the router to attend to the needed positions.
This approach mitigates the risk of overlooking essential contextual
information. (2) The router-only training strategy entails freezing LLM
parameters and exclusively updating routers for only a few steps. When applied
to open-source LLMs including Llama and Mistral, MoICE surpasses prior methods
across multiple tasks on long context understanding and generation, all while
maintaining commendable inference efficiency.
