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Empowering large language models to accurately express confidence in their answers is essential for trustworthy decision-making.
Are humans good intuitive statisticians after all? rethinking some conclusions from the literature on judgment under uncertainty
Leda Cosmides and John Tooby · 1996
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Language models are few-shot learners
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Mix-n-match: Ensemble and compositional methods for uncertainty calibration in deep learning
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al · 2021
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A survey of uncertainty in deep neural networks
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Revisiting the calibration of modern neural networks
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Are NLP models really able to solve simple math word problems?
Teaching models to express their uncertainty in words
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Reducing conversational agents’ overconfidence through linguistic calibration
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Out-of-distribution detection and selective generation for conditional language models
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Self-consistency improves chain of thought reasoning in language models
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Natural language processing and assessment of resident feedback quality
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