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As large language models (LLMs) have become the norm in NLP, demonstratinggood performance in generation and reasoning tasks, one of its most fataldisadvantages is the lack of factual correctness. Generating unfactual textsnot only leads to lower performances but also degrades the trust and validityof their applications. Chain-of-Thought (CoT) prompting improves trust andmodel performance on complex reasoning tasks by generating interpretablereasoning chains, but still suffers from factuality concerns inknowledge-intensive tasks. In this paper, we propose the Verify-and-Editframework for CoT prompting, which seeks to increase prediction factuality bypost-editing reasoning chains according to external knowledge. Building on topof GPT-3, our framework lead to accuracy improvements in multiple open-domainquestion-answering tasks.

Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework