Agent Instructs Large Language Models to be General Zero-Shot Reasoners
Nicholas Crispino Kyle Montgomery Fankun Zeng Dawn Song Chenguang Wang
We introduce a method to improve the zero-shot reasoning abilities of large
language models on general language understanding tasks. Specifically, we build
an autonomous agent to instruct the reasoning process of large language models.
We show this approach further unleashes the zero-shot reasoning abilities of
large language models to more tasks. We study the performance of our method on
a wide set of datasets spanning generation, classification, and reasoning. We
show that our method generalizes to most tasks and obtains state-of-the-art
zero-shot performance on 20 of the 29 datasets that we evaluate. For instance,
our method boosts the performance of state-of-the-art large language models by
a large margin, including Vicuna-13b (13.3%), Llama-2-70b-chat (23.2%), and
GPT-3.5 Turbo (17.0%). Compared to zero-shot chain of thought, our improvement
in reasoning is striking, with an average increase of 10.5%. With our method,
Llama-2-70b-chat outperforms zero-shot GPT-3.5 Turbo by 10.2%.