 have garnered widespread attention due to their remarkable performance across various tasks. However%2C to mitigate the issue of hallucinations%2C LLMs often incorporate retrieval-augmented pipeline to provide them with rich external knowledge and context. Nevertheless%2C challenges stem from inaccurate and coarse-grained context retrieved from the retriever. Supplying irrelevant context to the LLMs can result in poorer responses%2C increased inference latency%2C and higher costs. This paper introduces a method called Instruction-Aware Contextual Compression%2C which filters out less informative content%2C thereby accelerating and enhancing the use of LLMs. The experimental results demonstrate that Instruction-Aware Contextual Compression notably reduces memory consumption and minimizes generation latency while maintaining performance levels comparable to those achieved with the use of the full context. Specifically%2C we achieved a 50%25 reduction in context-related costs%2C resulting in a 5%25 reduction in inference memory usage and a 2.2-fold increase in inference speed%2C with only a minor drop of 0.047 in Rouge-1. These findings suggest that our method strikes an effective balance between efficiency and performance.?quality=80&w=1200)
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