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.

我们提出了 Re-Ex 方法，它通过加入一个称为事实错误解释步骤的新步骤，使用外部工具获取事实错误的证据，并让 LLMs 解释响应的问题部分，最后使用解释来修订响应，从而减少 LLMs 生成文本的幻觉问题。与 Factool、CoVE 和 RARR 等现有方法相比，Re-Ex 在多个基准测试中提供了更好的修订性能，且所需的时间和标记数量更少。