Efficient Conformal Prediction via Cascaded Inference with Expanded Admission
This work addresses efficiency issues in conformal prediction for open-ended classification tasks, such as in NLP and drug discovery, offering an incremental improvement over existing methods.
The paper tackles the problem of conformal prediction sets being too large and costly by expanding the correctness criterion to include inferred admissible answers and using prediction cascades to prune labels early, achieving valid performance guarantees with reduced set sizes.
In this paper, we present a novel approach for conformal prediction (CP), in which we aim to identify a set of promising prediction candidates -- in place of a single prediction. This set is guaranteed to contain a correct answer with high probability, and is well-suited for many open-ended classification tasks. In the standard CP paradigm, the predicted set can often be unusably large and also costly to obtain. This is particularly pervasive in settings where the correct answer is not unique, and the number of total possible answers is high. We first expand the CP correctness criterion to allow for additional, inferred "admissible" answers, which can substantially reduce the size of the predicted set while still providing valid performance guarantees. Second, we amortize costs by conformalizing prediction cascades, in which we aggressively prune implausible labels early on by using progressively stronger classifiers -- again, while still providing valid performance guarantees. We demonstrate the empirical effectiveness of our approach for multiple applications in natural language processing and computational chemistry for drug discovery.