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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