Differentiable Instruction Optimization for Cross-Task Generalization
Masaru Isonuma Junichiro Mori Ichiro Sakata
Instruction tuning has been attracting much attention to achieve
generalization ability across a wide variety of tasks. Although various types
of instructions have been manually created for instruction tuning, it is still
unclear what kind of instruction is optimal to obtain cross-task generalization
ability. This work presents instruction optimization, which optimizes training
instructions with respect to generalization ability. Rather than manually
tuning instructions, we introduce learnable instructions and optimize them with
gradient descent by leveraging bilevel optimization. Experimental results show
that the learned instruction enhances the diversity of instructions and
improves the generalization ability compared to using only manually created
instructions.