Rethinking with Retrieval: Faithful Large Language Model Inference

• URL: http://arxiv.org/abs/2301.00303v1
• Date: Sat, 31 Dec 2022 22:35:34 GMT
• Title: Rethinking with Retrieval: Faithful Large Language Model Inference
• Authors: Hangfeng He, Hongming Zhang, Dan Roth
• Abstract summary: We propose a novel post-processing approach, rethinking with retrieval (RR) RR retrieves relevant external knowledge based on the reasoning steps obtained from the chain-of-thought prompting. We evaluate the effectiveness of RR through extensive experiments with GPT-3 on three complex reasoning tasks.
• Score: 91.66406351103484
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the success of large language models (LLMs) in various natural language processing (NLP) tasks, the stored knowledge in these models may inevitably be incomplete, out-of-date, or incorrect. This motivates the need to utilize external knowledge to assist LLMs. Unfortunately, current methods for incorporating external knowledge often require additional training or fine-tuning, which can be costly and may not be feasible for LLMs. To address this issue, we propose a novel post-processing approach, rethinking with retrieval (RR), which retrieves relevant external knowledge based on the decomposed reasoning steps obtained from the chain-of-thought (CoT) prompting. This lightweight approach does not require additional training or fine-tuning and is not limited by the input length of LLMs. We evaluate the effectiveness of RR through extensive experiments with GPT-3 on three complex reasoning tasks: commonsense reasoning, temporal reasoning, and tabular reasoning. Our results show that RR can produce more faithful explanations and improve the performance of LLMs.

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