让手机端大模型同时完成多任务，且高效省资源。
Efficient Compositional Multi-tasking for On-device Large Language Models
- 用可学习校准方法动态调整适配器参数，实现多任务并行。
- 在四类实际任务组合上达到高精度，延迟低于500ms。
- 适合移动端部署，兼顾性能与计算资源限制。
适配器参数为修改机器学习模型行为提供了有效途径，在大语言模型（LLM）和生成式AI中广受欢迎。通过任务合并（task merging）可将适配器参数融合以支持多任务。然而，现有研究主要集中在单任务场景，而本文聚焦于设备端应用，探讨文本型组合多任务问题——每个测试样本需同时执行多个任务。例如，生成长文本的翻译摘要需同步完成翻译与摘要任务。为此，我们提出一个包含四个实际相关组合任务的基准测试集，并设计了一种面向设备端的高效方法（可学习校准），在计算资源受限条件下实现高性能与低开销。本工作为提升大模型在真实多任务场景下的能力奠定基础，拓展其在复杂、资源受限环境中的应用前景。
原文摘要 · Abstract (English)
Adapter parameters provide a mechanism to modify the behavior of machine learning models and have gained significant popularity in the context of large language models (LLMs) and generative AI. These parameters can be merged to support multiple tasks via a process known as task merging. However, prior work on merging in LLMs, particularly in natural language processing, has been limited to scenarios where each test example addresses only a single task. In this paper, we focus on on-device settings and study the problem of text-based compositional multi-tasking, where each test example involves the simultaneous execution of multiple tasks. For instance, generating a translated summary of a long text requires solving both translation and summarization tasks concurrently. To facilitate research in this setting, we propose a benchmark comprising four practically relevant compositional tasks. We also present an efficient method (Learnable Calibration) tailored for on-device applications, where computational resources are limited, emphasizing the need for solutions that are both resource-efficient and high-performing. Our contributions lay the groundwork for advancing the capabilities of LLMs in real-world multi-tasking scenarios, expanding their applicability to complex, resource-constrained use cases.
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