Large Language Model Confidence Estimation via Black-Box Access
Authors: name: Tejaswini Pedapati, name: Amit Dhurandhar, name: Soumya Ghosh, name: Soham Dan, name: Prasanna Sattigeri

Estimating uncertainty or confidence in the responses of a model can be
significant in evaluating trust not only in the responses, but also in the
model as a whole. In this paper, we explore the problem of estimating
confidence for responses of large language models (LLMs) with simply black-box
or query access to them. We propose a simple and extensible framework where, we
engineer novel features and train a (interpretable) model (viz. logistic
regression) on these features to estimate the confidence. We empirically
demonstrate that our simple framework is effective in estimating confidence of
flan-ul2, llama-13b and mistral-7b with it consistently outperforming existing
black-box confidence estimation approaches on benchmark datasets such as
TriviaQA, SQuAD, CoQA and Natural Questions by even over 10% (on AUROC) in
some cases. Additionally, our interpretable approach provides insight into
features that are predictive of confidence, leading to the interesting and
useful discovery that our confidence models built for one LLM generalize
zero-shot across others on a given dataset.