@article{campos-etal-2024-conformal,
    title = "Conformal Prediction for Natural Language Processing: A Survey",
    author = "Campos, Margarida  and
      Farinhas, Ant{\'o}nio  and
      Zerva, Chrysoula  and
      Figueiredo, M{\'a}rio A. T.  and
      Martins, Andr{\'e} F. T.",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "12",
    year = "2024",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2024.tacl-1.82/",
    doi = "10.1162/tacl_a_00715",
    pages = "1497--1516",
    abstract = "The rapid proliferation of large language models and natural language processing (NLP) applications creates a crucial need for uncertainty quantification to mitigate risks such as Hallucinations and to enhance decision-making reliability in critical applications. Conformal prediction is emerging as a theoretically sound and practically useful framework, combining flexibility with strong statistical guarantees. Its model-agnostic and distribution-free nature makes it particularly promising to address the current shortcomings of NLP systems that stem from the absence of uncertainty quantification. This paper provides a comprehensive survey of conformal prediction techniques, their guarantees, and existing applications in NLP, pointing to directions for future research and open challenges."
}