  Abstract reasoning, the ability to reason from the abstract essence of a
problem, serves as a key to generalization in human reasoning. However,
eliciting language models to perform reasoning with abstraction remains
unexplored. This paper seeks to bridge this gap by introducing a novel
structured reasoning format called Abstraction-of-Thought (AoT). The uniqueness
of AoT lies in its explicit requirement for varying levels of abstraction
within the reasoning process. This approach could elicit language models to
first contemplate on the abstract level before incorporating concrete details,
which is overlooked by the prevailing step-by-step Chain-of-Thought (CoT)
method. To align models with the AoT format, we present AoT Collection, a
generic finetuning dataset consisting of 348k high-quality samples with AoT
reasoning processes, collected via an automated and scalable pipeline. We
finetune a wide range of language models with AoT Collection and conduct
extensive evaluations on 23 unseen tasks from the challenging benchmark
Big-Bench Hard. Experimental results indicate that models aligned to AoT
reasoning format substantially outperform those aligned to CoT in many
reasoning tasks.
