 by skipping several layers of computations through elaborate heuristics or additional predictors. However%2C in the decoding process of existing approaches%2C different samples are assigned different computational budgets%2C which cannot guarantee a stable and precise acceleration effect. Furthermore%2C existing approaches generally skip multiple contiguous layers at the bottom or top of the layers%2C leading to a drastic change in the model's layer-wise representations%2C and thus a consequent performance degeneration. Therefore%2C we propose a Unified Layer Skipping strategy%2C which selects the number of layers to skip computation based solely on the target speedup ratio%2C and then skips the corresponding number of intermediate layer computations in a balanced manner. Since the Unified Layer Skipping strategy is independent of input samples%2C it naturally supports popular acceleration techniques such as batch decoding and KV caching%2C thus demonstrating more practicality for real-world applications. Experimental results on two common tasks%2C i.e.%2C machine translation and text summarization%2C indicate that given a target speedup ratio%2C the Unified Layer Skipping strategy significantly enhances both the inference performance and the actual model throughput over existing dynamic approaches.?quality=80&w=800)
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 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=800)
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