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Improved Confidence Estimates for Black-Box Large Language Models
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uncertainty quantificationlarge language modelsclassificationreal-world applications
2608.19323
Builder Relevance
2h ago80%
Abstract
This work demonstrates a method for improving uncertainty quantification in large language models by leveraging a dataset of interest to enhance confidence scores.
Reality Card
Core Claim
The proposed method consistently outperforms existing uncertainty quantification scores by using simple classifiers that incorporate LLM response correctness and related features.
Method / Result
Achieved improved performance in uncertainty quantification with minimal computational overhead.
Limitations
The method's effectiveness is contingent on the availability of a relevant dataset for evaluation.
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