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.

翻译：大语言模型及自然语言处理（NLP）应用的迅速普及，迫切需要量化不确定性以降低幻觉等风险并提升关键应用场景下决策的可靠性。共形预测正成为一种理论上严谨且实践有效的框架，兼具灵活性与强统计保证。其模型无关与无分布假设的特性，使其特别适用于解决当前NLP系统因缺乏不确定性量化而产生的缺陷。本文全面综述了共形预测技术、其理论保证以及目前在NLP中的应用，并指明了未来研究方向与待解决的关键挑战。