Training LLMs to verbalize uncertainty explicitly at the end or during reasoning reduces overconfident errors and improves answer quality on factual tasks while enabling RAG triggers.
We can’t understand ai using our existing vocabulary
2 Pith papers cite this work. Polarity classification is still indexing.
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CHiQPM is a hierarchical interpretable image classifier that maintains 99% of non-interpretable model accuracy while supplying contrastive global explanations, human-like hierarchical paths, and calibrated interpretable set predictions via conformal prediction.
citing papers explorer
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LLMs Should Express Uncertainty Explicitly
Training LLMs to verbalize uncertainty explicitly at the end or during reasoning reduces overconfident errors and improves answer quality on factual tasks while enabling RAG triggers.
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CHiQPM: Calibrated Hierarchical Interpretable Image Classification
CHiQPM is a hierarchical interpretable image classifier that maintains 99% of non-interpretable model accuracy while supplying contrastive global explanations, human-like hierarchical paths, and calibrated interpretable set predictions via conformal prediction.