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

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

5 May 2023arXiv:2305.03268archive 2025-07-28

Ruochen Zhao, Xingxuan Li, Shafiq Joty, Chengwei Qin, Lidong Bing

As large language models (LLMs) have become the norm in NLP, demonstrating good performance in generation and reasoning tasks, one of its most fatal disadvantages is the lack of factual correctness. Generating unfactual texts not only leads to lower performances but also degrades the trust and validity of their applications. Chain-of-Thought (CoT) prompting improves trust and model performance on complex reasoning tasks by generating interpretable reasoning chains, but still suffers from factuality concerns in knowledge-intensive tasks. In this paper, we propose the Verify-and-Edit framework for CoT prompting, which seeks to increase prediction factuality by post-editing reasoning chains according to external knowledge. Building on top of GPT-3, our framework lead to accuracy improvements in multiple open-domain question-answering tasks.

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Open-Domain Question AnsweringQuestion Answering

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AdamAttentionAttention DropoutBPECosine AnnealingDense ConnectionsDropoutGPT-3Layer NormalizationLinear LayerLinear Warmup With Cosine AnnealingMulti-Head AttentionResidual ConnectionSoftmaxWeight Decay

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