Conformal Prediction for Natural Language Processing: A Survey
<|reference_start|>Conformal Prediction for Natural Language Processing: A Survey: 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.<|reference_end|>
@article{campos2024conformal,
    title={Conformal Prediction for Natural Language Processing: A Survey},
    author={Margarida M. Campos, Ant'onio Farinhas, Chrysoula Zerva, M'ario A.T.
  Figueiredo, Andr'e F.T. Martins},
    journal={arXiv preprint arXiv:2405.01976},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.01976},
    primaryClass={cs.CL cs.LG}
}