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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