无需训练即可动态选择并合并LoRA适配器，提升多任务推理效果。
LoRA on the Go: Instance-level Dynamic LoRA Selection and Merging
- 基于单次前向传播信号，实时识别最相关适配器。
- 在27个数据集上最高提升3.6%，且保持推理速度。
- 适合需快速适配多场景的实用部署场景。
低秩适配（LoRA）已成为微调大语言模型的参数高效方法。然而，传统LoRA适配器通常针对单一任务训练，在真实场景中面对多样化、不可预测输入时适用性受限。现有推理阶段融合多LoRA的方法通常需标注数据或额外任务训练，成本较高。本文提出无需训练的LoRA on the Go（LoGo）框架，可在实例级别动态选择并合并适配器，无需额外要求。LoGo利用单次前向传播中提取的信号，实时判断最相关适配器及其贡献。在5个NLP基准、27个数据集和3个模型家族上，LoGo在部分任务上性能超越基于训练的基线最高达3.6%，其余任务表现相当，同时维持推理吞吐量，展现出高效与实用性。
原文摘要 · Abstract (English)
Low-Rank Adaptation (LoRA) has emerged as a parameter-efficient approach for fine-tuning large language models. However, conventional LoRA adapters are typically trained for a single task, limiting their applicability in real-world settings where inputs may span diverse and unpredictable domains. At inference time, existing approaches combine multiple LoRAs for improving performance on diverse tasks, while usually requiring labeled data or additional task-specific training, which is expensive at scale. In this work, we introduce LoRA on the Go (LoGo), a training-free framework that dynamically selects and merges adapters at the instance level without any additional requirements. LoGo leverages signals extracted from a single forward pass through LoRA adapters, to identify the most relevant adapters and determine their contributions on-the-fly. Across 5 NLP benchmarks, 27 datasets, and 3 model families, LoGo outperforms training-based baselines on some tasks upto a margin of 3.6% while remaining competitive on other tasks and maintaining inference throughput, highlighting its effectiveness and practicality.
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