Re-Ex: Revising after Explanation Reduces the Factual Errors in LLM Responses

Mitigating hallucination issues is one of the main challenges of LLMs we need to overcome, in order to reliably use them in real-world scenarios. Recently, various methods are proposed to check the factual errors in the LLM-generated texts and revise them accordingly, to reduce the hallucination issue. In this paper, we propose Re-Ex, a method of revising LLM-generated texts, which introduces a novel step dubbed as the factual error explanation step. Re-Ex revises the initial response of LLMs using 3-steps: first, external tools are used to get the evidences on the factual errors in the response; second, LLMs are instructed to explain the problematic parts of the response based on the evidences gathered in the first step; finally, LLMs revise the response using the explanation obtained in the second step. In addition to the explanation step, we propose new prompting techniques to reduce the amount of tokens and wall-clock time required for the response revision process. Compared with existing methods including Factool, CoVE, and RARR, Re-Ex provides better revision performance with less time and fewer tokens in multiple benchmarks.