Contrastive Chain-of-Thought Prompting
Yew Ken Chia Guizhen Chen Luu Anh Tuan Soujanya Poria Lidong Bing
Despite the success of chain of thought in enhancing language model
reasoning, the underlying process remains less well understood. Although
logically sound reasoning appears inherently crucial for chain of thought,
prior studies surprisingly reveal minimal impact when using invalid
demonstrations instead. Furthermore, the conventional chain of thought does not
inform language models on what mistakes to avoid, which potentially leads to
more errors. Hence, inspired by how humans can learn from both positive and
negative examples, we propose contrastive chain of thought to enhance language
model reasoning. Compared to the conventional chain of thought, our approach
provides both valid and invalid reasoning demonstrations, to guide the model to
reason step-by-step while reducing reasoning mistakes. To improve
generalization, we introduce an automatic method to construct contrastive
demonstrations. Our experiments on reasoning benchmarks demonstrate that
contrastive chain of thought can serve as a general enhancement of
chain-of-thought prompting.