Contrastive Chain-of-Thought Prompting
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.
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Yew Ken Chia
6556de838076124b8d7fbcaf
Guizhen Chen
admin_assigned
2023-11-17T16:04:33.364Z
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Chen
user
Guizhen
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Luu Anh Tuan
admin_assigned
2023-11-17T16:04:40.502Z
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Luu Anh Tuan
user
anhtuanluu36
6556de838076124b8d7fbcb1
Soujanya Poria
admin_assigned
2023-11-17T16:04:47.216Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/626b626405fe1cb65725aca1/aa-Lata46I3fXOmMetvXH.jpeg
Soujanya Poria
user
soujanyaporia
6556de838076124b8d7fbcb2
Lidong Bing
admin_assigned
2023-11-17T16:04:55.095Z
6454685a548f22be598414c4
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/eMjMWKJ-AouF7eY1-RzGF.jpeg
Lidong Bing
user
LidongBing
Contrastive Chain-of-Thought Prompting
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.
Contrastive chain of thought, utilizing both valid and invalid reasoning examples, improves language model reasoning and generalization compared to conventional methods.
chain of thought reasoning contrastive chain of thought demonstrations reasoning mistakes generalization