Re-Ex: Revising after Explanation Reduces the Factual Errors in LLM Responses
<|reference_start|>Re-Ex: Revising after Explanation Reduces the Factual Errors in LLM Responses: 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.<|reference_end|>
@article{kim2024re-ex:,
    title={Re-Ex: Revising after Explanation Reduces the Factual Errors in LLM
  Responses},
    author={Juyeon Kim, Jeongeun Lee, Yoonho Chang, Chanyeol Choi, Junseong Kim,
  Jy-yong Sohn},
    journal={arXiv preprint arXiv:2402.17097},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.17097},
    primaryClass={cs.CL cs.AI}
}