CONFIDE applies conformal prediction to transformer embeddings for valid prediction sets, improving accuracy up to 4.09% and efficiency over baselines on models like BERT-tiny.
Criteria of efficiency for conformal prediction
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
abstract
We study optimal conformity measures for various criteria of efficiency of classification in an idealised setting. This leads to an important class of criteria of efficiency that we call probabilistic; it turns out that the most standard criteria of efficiency used in literature on conformal prediction are not probabilistic unless the problem of classification is binary. We consider both unconditional and label-conditional conformal prediction.
fields
cs.LG 2verdicts
UNVERDICTED 2representative citing papers
libconform v0.1.0 is a Python library that implements core conformal prediction algorithms and exposes a documented API.
citing papers explorer
-
Uncertainty-Aware Transformers: Conformal Prediction for Language Models
CONFIDE applies conformal prediction to transformer embeddings for valid prediction sets, improving accuracy up to 4.09% and efficiency over baselines on models like BERT-tiny.
-
libconform v0.1.0: a Python library for conformal prediction
libconform v0.1.0 is a Python library that implements core conformal prediction algorithms and exposes a documented API.