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Concept bottleneck models can now distinguish between reducible model uncertainty and irreducible input ambiguity, enabling targeted interventions like data collection and human review.
Learning from graded plausibility labels is now tractable: a novel KL-projection method efficiently transforms possibility distributions into probability distributions, improving classification performance on noisy NLI data.
