  Mitigating hallucination issues is a key challenge that must be overcome to
reliably deploy large language models (LLMs) in real-world scenarios. Recently,
various methods have been proposed to detect and revise factual errors in
LLM-generated texts, in order to reduce hallucination. In this paper, we
propose Re-Ex, a method for post-editing LLM-generated responses. Re-Ex
introduces a novel reasoning 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 retrieve the evidences of the factual errors in the initial
LLM response; next, LLM is instructed to explain the problematic parts of the
response based on the gathered evidence; finally, LLM revises the initial
response using the explanations provided in the previous step. In addition to
the explanation step, Re-Ex also incorporates new prompting techniques to
reduce the token count and inference time required for the response revision
process. Compared with existing methods including FacTool, CoVE, and RARR,
Re-Ex provides better detection and revision performance with less inference
time and fewer tokens in multiple benchmarks.
