Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logic

arXiv:2309.13339v2 Announce Type: replace-cross
Abstract: Recent advancements in large language models have showcased their remarkable generalizability across various domains. However, their reasoning abilities still have significant room for improvement, especially when confronted with scenarios requiring multi-step reasoning. Although large language models possess extensive knowledge, their reasoning often fails to effectively utilize this knowledge to establish a coherent thinking paradigm. These models sometimes show hallucinations as their reasoning procedures are unconstrained by logical principles. Aiming at improving the zero-shot chain-of-thought reasoning …

abstract arxiv cs.ai cs.cl cs.lg cs.sc domains improvement knowledge language language models large language large language models logic reasoning room thought through type zero-shot