LoraRetriever: Input-Aware LoRA Retrieval and Composition for Mixed
  Tasks in the Wild
Ziyu Zhao Leilei Gan Guoyin Wang Wangchunshu Zhou Hongxia Yang Kun Kuang Fei Wu
Low-Rank Adaptation (LoRA) provides an effective yet efficient solution for
fine-tuning large language models (LLM). The modular and plug-and-play nature
of LoRA enables the integration of diverse domain-specific LoRAs to enhance the
capabilities of LLMs. Previous research on exploiting multiple LoRAs either
focuses on specific isolated downstream tasks or fixes the selection of LoRAs
during training. However, in real-world scenarios, LLMs receive diverse prompts
covering different tasks, and the pool of candidate LoRAs is often dynamically
updated. To bridge this gap, we propose LoraRetriever, a retrieve-then-compose
framework that adaptively retrieves and composes multiple LoRAs according to
the input prompts. LoraRetriever contains three main components: firstly,
identifying and retrieving LoRAs relevant to the given input; secondly,
formulating strategies for effectively integrating the retrieved LoRAs; and
thirdly, developing efficient batch inference to accommodate heterogeneous
requests. Experimental results indicate that LoraRetriever consistently
outperforms the baselines, highlighting its practical effectiveness and
versatility.