Self Consistency

Large Language Models Can Self-Improve

Huang et al. (2022) show that LLMs can bootstrap their own reasoning ability by generating chain-of-thought rationales, filtering with self-consistency majority vote, and finetuning on the high-confidence outputs — no human labels needed. A clean demonstration that LLMs can improve themselves without supervised signal.