title: Llamas Know What GPTs Don't Show: Surrogate Models for Confidence
    Estimation
summary: 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).
paper_authors._id: 6555aa48d5a72476aef11f0b
paper_authors.name: Vaishnavi Shrivastava
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-16T10:18:08.677Z
paper_authors._id: 63b75a016fc56e43c3c15980
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1672960383634-noauth.jpeg
paper_authors.fullname: Vaishnavi Shrivastava
paper_authors.type: user
paper_authors.user: vshrivas
paper_authors._id: 6555aa48d5a72476aef11f0c
paper_authors.name: Percy Liang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-16T10:18:15.825Z
paper_authors._id: 6409651b9e9f790c905b2335
paper_authors.avatarUrl: /avatars/1fb8c80b60f21f65a0a027319101f236.svg
paper_authors.fullname: Percy Liang
paper_authors.type: user
paper_authors.user: percyliang
paper_authors._id: 6555aa48d5a72476aef11f0d
paper_authors.name: Ananya Kumar
paper_title: Llamas Know What GPTs Don't Show: Surrogate Models for Confidence
    Estimation
paper_summary: 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).
paper_ai_keywords: large language models (LLMs) softmax probabilities state-of-the-art LLMs GPT-4 Claude-v1.3 eliciting confidence linguistically surrogate confidence model question-answering datasets AUC random baseline linguistic confidences surrogate model probabilities state-of-the-art confidence estimates GPT-4 average AUC