Ada-Instruct: Adapting Instruction Generators for Complex Reasoning

• URL: http://arxiv.org/abs/2310.04484v2
• Date: Tue, 10 Oct 2023 07:17:32 GMT
• Title: Ada-Instruct: Adapting Instruction Generators for Complex Reasoning
• Authors: Wanyun Cui, Qianle Wang
• Abstract summary: We introduce Ada-Instruct, an adaptive instruction generator developed by fine-tuning open-source LLMs. We empirically validated Ada-Instruct's efficacy across different applications, including code completion, mathematical reasoning, and commonsense reasoning.
• Score: 17.07852413707166
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating diverse and sophisticated instructions for downstream tasks by Large Language Models (LLMs) is pivotal for advancing the effect. Current approaches leverage closed-source LLMs, employing in-context prompting for instruction generation. However, in this paper, we found that in-context prompting cannot generate complex instructions with length $\ge 100$ for tasks like code completion. To solve this problem, we introduce Ada-Instruct, an adaptive instruction generator developed by fine-tuning open-source LLMs. Our pivotal finding illustrates that fine-tuning open-source LLMs with a mere ten samples generates long instructions that maintain distributional consistency for complex reasoning tasks. We empirically validated Ada-Instruct's efficacy across different applications, including code completion, mathematical reasoning, and commonsense reasoning. The results underscore Ada-Instruct's superiority, evidencing its improvements over its base models, current self-instruct methods, and other state-of-the-art models.

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