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We introduce BSDetector, a method for detecting bad and speculative answersfrom a pretrained Large Language Model by estimating a numeric confidence scorefor any output it generated. Our uncertainty quantification technique works forany LLM accessible only via a black-box API, whose training data remainsunknown. By expending a bit of extra computation, users of any LLM API can nowget the same response as they would ordinarily, as well as a confidenceestimate that cautions when not to trust this response. Experiments on bothclosed and open-form Question-Answer benchmarks reveal that BSDetector moreaccurately identifies incorrect LLM responses than alternative uncertaintyestimation procedures (for both GPT-3 and ChatGPT). By sampling multipleresponses from the LLM and considering the one with the highest confidencescore, we can additionally obtain more accurate responses from the same LLM,without any extra training steps. In applications involving automatedevaluation with LLMs, accounting for our confidence scores leads to morereliable evaluation in both human-in-the-loop and fully-automated settings(across both GPT 3.5 and 4).

Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness