Researchers propose Semantic Self-Consistency to improve LLM reasoning
A new paper on arXiv introduces semantic self-consistency, a method that builds on the existing self-consistency framework for large language models. Standard self-consistency samples multiple reasoning paths and takes a majority vote on the final answers. The authors argue this ignores the semantic information contained in the step-by-step reasoning paths themselves. Their approach analyzes…
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Key points
- Paper introduces semantic self-consistency for LLM reasoning
- Method analyzes reasoning paths, not just final answers
- Claims improved reliability on complex reasoning tasks
Read the original at arXiv cs.CL · by Tim Knappe, Ryan Li, Ayush Chauhan, Kaylee Chhua, Kevin Zhu, Sean O'Brien primary sourceOpen source ↗
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Wang et al.
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