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arXiv · 2405.01976

Conformal Prediction for Natural Language Processing: A Survey

Margarida M. Campos·António Farinhas·Chrysoula Zerva·Mário A. T. Figueiredo·André F. T. Martins
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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.

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Margarida M. Campos, António Farinhas, Chrysoula Zerva, Mário A. T. Figueiredo, André F. T. Martins. 2024-05-03. Conformal Prediction for Natural Language Processing: A Survey. https://arxiv.org/abs/2405.01976

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