Ada-Instruct: Adapting Instruction Generators for Complex Reasoning

摘要

Instructions augmentation is a crucial step for unleashing the full potential of large language models (LLMs) in downstream tasks. Existing Self-Instruct methods primarily simulate new instructions from a few initial instructions with in-context learning. However, our study identifies a critical flaw in this approach: even with GPT4o, it cannot generate complex instructions of length \ge 100, which is necessary in complex tasks such as code completion.To address this issue, our key insight is that fine-tuning open source LLMs with only ten examples can produce complex instructions that maintain distributional consistency for complex reasoning tasks. We introduce Ada-Instruct, an adaptive instruction generator developed through fine-tuning. We empirically validated Ada-Instruct’s efficacy across different applications. The results highlight Ada-Instruct’s capacity to generate long, intricate, and distributionally consistent instructions.