RevMUX：面向高效大语言模型批量推理的带可逆适配器的数据复用方法
RevMUX: Data Multiplexing with Reversible Adapters for Efficient LLM Batch Inference
AI总结：
针对大语言模型高吞吐量下并发查询处理难、传统数据复用需训练全主干且性能下降的问题，提出带可逆适配器的参数高效数据复用框架RevMUX，可在提升推理效率的同时保持良好分类性能。
摘要：
大语言模型（LLM）为自然语言处理（NLP）领域带来了重大突破，但由于其高吞吐量需求，也带来了处理并发用户查询的挑战。数据复用通过将多个输入合并为单个复合输入来解决这一问题，借助单次共享前向传播实现更高效的推理。然而，由于从复合输入中区分单个输入难度较大，传统方法通常需要训练整个主干网络，且仍存在性能下降的问题。本文提出RevMUX，这是一种参数高效的数据复用框架，在复用器中引入了可逆设计，解复用器可复用该设计执行反向操作，恢复单个样本以用于分类任务。在4个数据集和3类LLM主干上开展的大量实验表明，RevMUX能够有效提升LLM推理效率，同时保持令人满意的分类性能。
英文摘要：
Large language models (LLMs) have brought a great breakthrough to the natural language processing (NLP) community, while leading the challenge of handling concurrent customer queries due to their high throughput demands. Data multiplexing addresses this by merging multiple inputs into a single composite input, allowing more efficient inference through a shared forward pass. However, as distinguishing individuals from a composite input is challenging, conventional methods typically require training the entire backbone, yet still suffer from performance degradation. In this paper, we introduce RevMUX, a parameter-efficient data multiplexing framework that incorporates a reversible design in the multiplexer, which can be reused by the demultiplexer to perform reverse operations and restore individual samples for classification. Extensive experiments on four datasets and three types of LLM backbones demonstrate the effectiveness of RevMUX for enhancing LLM inference efficiency while retaining a satisfactory classification performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
