Conformal Prediction for Natural Language Processing: A Survey

• URL: http://arxiv.org/abs/2405.01976v1
• Date: Fri, 3 May 2024 10:00:45 GMT
• Title: Conformal Prediction for Natural Language Processing: A Survey
• Authors: Margarida M. Campos, António Farinhas, Chrysoula Zerva, Mário A. T. Figueiredo, André F. T. Martins,
• Abstract summary: Conformal prediction is emerging as a theoretically sound and practically useful framework. Its model-agnostic and distribution-free nature makes it particularly promising to address the current shortcomings of NLP systems. This paper provides a comprehensive survey of conformal prediction techniques, their guarantees, and existing applications in NLP.
• Score: 23.638214012459425
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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