Summary of Conformal Prediction For Natural Language Processing: a Survey, by Margarida M. Campos et al.
Conformal Prediction for Natural Language Processing: A Survey
by Margarida M. Campos, António Farinhas, Chrysoula Zerva, Mário A.T. Figueiredo, André F.T. Martins
First submitted to arxiv on: 3 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The rapid proliferation of large language models and natural language processing (NLP) applications has created a crucial need for uncertainty quantification to mitigate risks such as hallucinations and enhance decision-making reliability in critical applications. This paper provides a comprehensive survey of conformal prediction techniques, their guarantees, and existing applications in NLP, highlighting the framework’s model-agnostic and distribution-free nature that makes it particularly promising to address current shortcomings of NLP systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Conformal prediction is a way to add uncertainty to language models. This helps prevent bad things from happening when we use these models to make decisions. Right now, most language processing applications don’t have this uncertainty, which can lead to big problems. Conformal prediction solves this by giving us a way to understand how certain our answers are. This paper talks about the different ways we can do this and shows some examples where it works well. |
Keywords
» Artificial intelligence » Natural language processing » Nlp
