C3oT: Generating Shorter Chain-of-Thought without Compromising Effectiveness

Understand

Generating Chain-of-Thought (CoT) before deriving the answer can effectively improve the reasoning capabilities of large language models (LLMs) and significantly improve the accuracy of the generated answer.

• However, in most cases, the length of the generated CoT is much longer than the desired final answer, which results in additional decoding costs.
• Furthermore, existing research has discovered that shortening the reasoning steps in CoT, even while preserving the key information, diminishes LLMs' abilities.
• These phenomena make it difficult to use LLMs and CoT in many real-world applications that only require the final answer and are sensitive to latency, such as search and recommendation.