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.

我们提出了对比思维链来增强语言模型推理，通过提供有效和无效的推理示范，引导模型逐步推理并减少推理错误。在推理基准测试中，对比思维链能够作为思维链提示的一种普遍增强。

对比的思维链路提示

Contrastive Chain-of-Thought Prompting

Chain-of-Thought (CoT) prompting can dramatically improve the multi-step
reasoning abilities of large language models (LLMs). CoT explicitly encourages
the LLM to generate intermediate rationales for solving a problem, by providing
a series of reasoning steps in the demonstrations. Despite its success, there
is still little understanding of what makes CoT prompting effective and which
aspects of the demonstrated reasoning steps contribute to its performance. In
this paper, we show that CoT reasoning is possible even with invalid
demonstrations - prompting with invalid reasoning steps can achieve over 80-90%
of the performance obtained using CoT under various metrics, while still
generating coherent lines of reasoning during inference. Further experiments
show that other aspects of the rationales, such as being relevant to the query
and correctly ordering the reasoning steps, are much more important for
effective CoT reasoning. Overall, these findings both deepen our understanding
of CoT prompting, and open up new questions regarding LLMs' capability to learn
to reason in context.

本论文研究了 Chain-of-Thought 提示对大型语言模型的多步推理能力的影响，发现即使使用无效的推理步骤，其提示也能达到 80-90% 的性能，并探究了其他理性方面的影响。