  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.
