Revisiting Chain-of-Thought Reasoning under Limited Supervision: Semi-supervised Chain-of-Thought Learning

Researchers have introduced a semi-supervised learning method designed to improve the reasoning capabilities of large language models without requiring fully labeled datasets. By training models to generate their own chain-of-thought sequences using limited supervision, this approach aims to reduce the dependency on human-annotated reasoning examples.