Fine-tuning pre-trained large language models (LLMs) on a diverse array of tasks has become a common approach for building models that can solve various natural language processing (NLP) tasks. However, where and to what extent these models retain task-specific knowledge remains largely unexplored. This study investigates the task-specific information encoded in pre-trained LLMs and the effects of instruction tuning on their representations across a diverse set of over 60 NLP tasks. We use a set of matrix analysis tools to examine the differences between the way pre-trained and instruction-tuned LLMs store task-specific information. Our findings reveal that while some tasks are already encoded within the pre-trained LLMs, others greatly benefit from instruction tuning. Additionally, we pinpointed the layers in which the model transitions from high-level general representations to more task-oriented representations. This finding extends our understanding of the governing mechanisms of LLMs and facilitates future research in the fields of parameter-efficient transfer learning and multi-task learning.

本研究解决了预训练大型语言模型在多任务学习中保持任务特定知识的程度与位置尚未明确的问题。通过矩阵分析工具，本研究发现指令调优显著影响模型的任务表示，并识别了模型从高层次通用表示转向更具任务导向表示的具体层次。这一发现丰富了我们对大型语言模型机制的理解，并为参数高效转移学习和多任务学习的未来研究奠定了基础。

逐层揭示指令调优的大型语言模型中的多任务学习发生位置