Llamas Know What GPTs Don't Show: Surrogate Models for Confidence
  Estimation
Vaishnavi Shrivastava Percy Liang Ananya Kumar
To maintain user trust, large language models (LLMs) should signal low
confidence on examples where they are incorrect, instead of misleading the
user. The standard approach of estimating confidence is to use the softmax
probabilities of these models, but as of November 2023, state-of-the-art LLMs
such as GPT-4 and Claude-v1.3 do not provide access to these probabilities. We
first study eliciting confidence linguistically -- asking an LLM for its
confidence in its answer -- which performs reasonably (80.5% AUC on GPT-4
averaged across 12 question-answering datasets -- 7% above a random baseline)
but leaves room for improvement. We then explore using a surrogate confidence
model -- using a model where we do have probabilities to evaluate the original
model's confidence in a given question. Surprisingly, even though these
probabilities come from a different and often weaker model, this method leads
to higher AUC than linguistic confidences on 9 out of 12 datasets. Our best
method composing linguistic confidences and surrogate model probabilities gives
state-of-the-art confidence estimates on all 12 datasets (84.6% average AUC on
GPT-4).