@inproceedings{liu-etal-2024-llms-learn-uncertainty,
    title = "Can {LLM}s Learn Uncertainty on Their Own? Expressing Uncertainty Effectively in A Self-Training Manner",
    author = "Liu, Shudong  and
      Li, Zhaocong  and
      Liu, Xuebo  and
      Zhan, Runzhe  and
      Wong, Derek F.  and
      Chao, Lidia S.  and
      Zhang, Min",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.emnlp-main.1205/",
    doi = "10.18653/v1/2024.emnlp-main.1205",
    pages = "21635--21645",
    abstract = "Large language models (LLMs) often exhibit excessive, random, and uninformative uncertainty, rendering them unsuitable for decision-making in human-computer interactions. In this paper, we aim to instigate a heightened awareness of self-uncertainty in LLMs, enabling them to express uncertainty more effectively. To accomplish this, we propose an uncertainty-aware instruction tuning (UaIT) method, aligning LLMs' perception with the probabilistic uncertainty of the generation. We conducted experiments using LLaMA2 and Mistral on multiple free-form QA tasks. Experimental results revealed a surprising 45.2{\%} improvement in the effectiveness of uncertainty expression by LLMs, accompanied by reasonably good out-of-domain generalization capabilities. Moreover, this uncertainty expression can serve as a valuable real-time basis for human decision-making, e.g., retrieving external documents and incorporating stronger LLMs."
}