@inproceedings{liu-etal-2024-trustworthiness,
    title = "Trustworthiness and Self-awareness in Large Language Models: An Exploration through the Think-Solve-Verify Framework",
    author = "Liu, Zhendong  and
      Xia, Changhong  and
      He, Wei  and
      Wang, Chongjun",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.lrec-main.1465/",
    pages = "16855--16866",
    abstract = "As Large Language Models (LLMs) become increasingly influential in reasoning tasks, ensuring their trustworthiness and introspective self-awareness is critical. This research introduces the Think-Solve-Verify (TSV) framework, an innovative strategy tailored to explore LLMs' trustworthiness, introspective self-awareness, and collaborative reasoning. This method accentuates a model{'}s capability to construct introspective reasoning processes from answers and ensure their trustworthiness. The reasoning with TSV consistently performs at or near the top across the majority of datasets with a single interaction with LLM. Moreover, we refine the voting process of self-consistency within the Chain-of-Thought (CoT) approach, leading to notable accuracy enhancements. In our evaluations, this approach improved performance from 67.3{\%} to 72.8{\%} on the AQuA dataset. Furthermore, we delve into the model{'}s ability to explain the given answers, highlighting the significance of discerning genuine comprehension from mere guesswork."
}