  Instruction-based language modeling has received significant attention in
pretrained language models. However, the efficiency of instruction engineering
remains low and hinders the development of instruction studies. Recent studies
have focused on automating instruction generation, but they primarily aim to
improve performance without considering other crucial objectives that impact
instruction quality, such as instruction length and perplexity. Therefore, we
propose a novel approach (i.e., InstOptima) that treats instruction generation
as an evolutionary multi-objective optimization problem. In contrast to text
edition-based methods, our approach utilizes a large language model (LLM) to
simulate instruction operators, including mutation and crossover. Furthermore,
we introduce an objective-guided mechanism for these operators, allowing the
LLM to comprehend the objectives and enhance the quality of the generated
instructions. Experimental results demonstrate improved fine-tuning performance
and the generation of a diverse set of high-quality instructions.
