@inproceedings{li-etal-2024-think,
    title = "Think Twice Before Trusting: Self-Detection for Large Language Models through Comprehensive Answer Reflection",
    author = "Li, Moxin  and
      Wang, Wenjie  and
      Feng, Fuli  and
      Zhu, Fengbin  and
      Wang, Qifan  and
      Chua, Tat-Seng",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.findings-emnlp.693/",
    doi = "10.18653/v1/2024.findings-emnlp.693",
    pages = "11858--11875",
    abstract = "Self-detection for Large Language Models (LLMs) seeks to evaluate the trustworthiness of the LLM`s output by leveraging its own capabilities, thereby alleviating the issue of output hallucination. However, existing self-detection approaches only retrospectively evaluate answers generated by LLM, typically leading to the over-trust in incorrectly generated answers. To tackle this limitation, we propose a novel self-detection paradigm that considers the comprehensive answer space beyond LLM-generated answers. It thoroughly compares the trustworthiness of multiple candidate answers to mitigate the over-trust in LLM-generated incorrect answers. Building upon this paradigm, we introduce a two-step framework, which firstly instructs LLM to reflect and provide justifications for each candidate answer, and then aggregates the justifications for comprehensive target answer evaluation. This framework can be seamlessly integrated with existing approaches for superior self-detection. Extensive experiments on six datasets spanning three tasks demonstrate the effectiveness of the proposed framework."
}