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About this paper
Dingjun Wu, Jing Zhang, Xinmei Huang
This paper introduces Chain of Thought Knowledge Augmentation (CoT-KA), a method that uses reasoning chains generated by large language models (LLMs) to enhance the performance of smaller, task-specific models. The approach streamlines knowledge transfer by directly augmenting training data with LLM-generated CoTs, leading to consistent performance improvements across various reasoning tasks without requiring external knowledge bases.
Key insights
- Chain of Thought (CoT) reasoning steps generated by large language models (LLMs) can be effectively transferred as a form of knowledge to smaller, task-specific models.
- The CoT-KA approach simplifies knowledge augmentation by eliminating the need for external knowledge bases and separate retrieval mechanisms, treating LLMs as direct sources of reasoning knowledge.
- While more CoTs generally improve performance, there are diminishing returns, and the optimal number of CoTs can be task-dependent.
Summary by alphaXiv.
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