Conformal Prediction for Natural Language Processing: A Survey
Abstrak
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.
Penulis (5)
Margarida Campos
António Farinhas
Chrysoula Zerva
Mário A. T. Figueiredo
André F. T. Martins
Akses Cepat
- Tahun Terbit
- 2024
- Bahasa
- en
- Total Sitasi
- 17×
- Sumber Database
- CrossRef
- DOI
- 10.1162/tacl_a_00715
- Akses
- Open Access ✓