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About this paper
Antonios Valkanas, Soumyasundar Pal, Pavel Rumiantsev, Yingxue Zhang, Mark Coates
C3PO introduces a self-supervised framework for optimizing Large Language Model cascades, enabling complex reasoning while adhering to probabilistic cost constraints. It learns decision rules without labeled data and provides theoretical guarantees on inference cost and generalization error, achieving accuracy comparable to the most powerful model at substantially reduced costs across various benchmarks.
Key insights
- Effective cascade decision rules and probabilistic cost control can be learned without ground-truth labels by defining regret relative to the Most Powerful Model's prediction.
- A discrete optimization over cascade exit thresholds, combined with conformal prediction for cost calibration, can provide rigorous theoretical guarantees on inference cost control.
- PAC-Bayesian analysis offers a robust method to derive generalization bounds for label-free cascade optimization, ensuring predictable performance on unseen data.
Summary by alphaXiv.
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