title: Contrastive Chain-of-Thought Prompting
summary: 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.
paper_authors._id: 6556de838076124b8d7fbcae
paper_authors.name: Yew Ken Chia
paper_authors._id: 6556de838076124b8d7fbcaf
paper_authors.name: Guizhen Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-17T16:04:33.364Z
paper_authors._id: 64e85b3edb3767299865e0e3
paper_authors.avatarUrl: /avatars/fdbe121535dea940edd2766161393485.svg
paper_authors.fullname: Chen
paper_authors.type: user
paper_authors.user: Guizhen
paper_authors._id: 6556de838076124b8d7fbcb0
paper_authors.name: Luu Anh Tuan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-17T16:04:40.502Z
paper_authors._id: 655722e80438e0854fae7554
paper_authors.avatarUrl: /avatars/b93a74f7c7880f9fe0f3ffb47e2aef5e.svg
paper_authors.fullname: Luu Anh Tuan
paper_authors.type: user
paper_authors.user: anhtuanluu36
paper_authors._id: 6556de838076124b8d7fbcb1
paper_authors.name: Soujanya Poria
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-17T16:04:47.216Z
paper_authors._id: 626b626405fe1cb65725aca1
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/626b626405fe1cb65725aca1/aa-Lata46I3fXOmMetvXH.jpeg
paper_authors.fullname: Soujanya Poria
paper_authors.type: user
paper_authors.user: soujanyaporia
paper_authors._id: 6556de838076124b8d7fbcb2
paper_authors.name: Lidong Bing
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-17T16:04:55.095Z
paper_authors._id: 6454685a548f22be598414c4
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/eMjMWKJ-AouF7eY1-RzGF.jpeg
paper_authors.fullname: Lidong Bing
paper_authors.type: user
paper_authors.user: LidongBing
paper_title: Contrastive Chain-of-Thought Prompting
paper_summary: 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.
paper_ai_summary: Contrastive chain of thought, utilizing both valid and invalid reasoning examples, improves language model reasoning and generalization compared to conventional methods.
paper_ai_keywords: chain of thought reasoning contrastive chain of thought demonstrations reasoning mistakes generalization