Large language models (LLMs) achieved remarkable performance across various
tasks. However, they face challenges in managing long documents and extended
conversations, due to significantly increased computational requirements, both
in memory and inference time, and potential context truncation when the input
exceeds the LLM's fixed context length. This paper proposes a method called
Selective Context that enhances the inference efficiency of LLMs by identifying
and pruning redundancy in the input context to make the input more compact. We
test our approach using common data sources requiring long context processing:
arXiv papers, news articles, and long conversations, on tasks of summarisation,
question answering, and response generation. Experimental results show that
Selective Context significantly reduces memory cost and decreases generation
latency while maintaining comparable performance compared to that achieved when
full context is used. Specifically, we achieve a 50\% reduction in context
cost, resulting in a 36\% reduction in inference memory usage and a 32\%
reduction in inference time, while observing only a minor drop of .023 in
BERTscore and .038 in faithfulness on four downstream applications, indicating
that our method strikes a good balance between efficiency and performance.

使用选择性上下文方法（Selective Context）可以显著提高大型语言模型（LLMs）的推理效率，减少内存占用和推理时间，并在维持可比较性能的基础上实现对上下文成本的 50％降低，36％的推理内存使用率降低以及 32％的推理时间降低。