Conformal Prediction for Natural Language Processing: A Survey

TL;DR

This survey reviews conformal prediction methods in NLP, highlighting their theoretical guarantees, practical applications, and potential to improve uncertainty quantification in large language models.

Contribution

It provides a comprehensive overview of conformal prediction techniques in NLP, emphasizing their guarantees, applications, and future research directions.

Findings

Conformal prediction offers strong statistical guarantees for NLP uncertainty quantification.

It is model-agnostic and distribution-free, suitable for diverse NLP tasks.

The survey identifies open challenges and future research directions.

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.