title: Large Language Model Confidence Estimation via Black-Box Access
summary: 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.
paper_authors._id: 6666747494d0f40432a9217c
paper_authors.name: Tejaswini Pedapati
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-10T11:48:14.348Z
paper_authors._id: 663272af2a4742700eb9fc4e
paper_authors.avatarUrl: /avatars/aaa988209a7a7b1607885247da399a9a.svg
paper_authors.fullname: Tejaswini Pedapati
paper_authors.type: user
paper_authors.user: tejaswinipedapati
paper_authors._id: 6666747494d0f40432a9217d
paper_authors.name: Amit Dhurandhar
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-10T11:48:20.667Z
paper_authors._id: 65fc84fe49382fd61f828862
paper_authors.avatarUrl: /avatars/80fe08a14493e2a83464de8d9aab010b.svg
paper_authors.fullname: Amit Dhurandhar
paper_authors.type: user
paper_authors.user: sadhamanus
paper_authors._id: 6666747494d0f40432a9217e
paper_authors.name: Soumya Ghosh
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-10T11:49:14.384Z
paper_authors._id: 65d11bc5bf849f3b79571771
paper_authors.avatarUrl: /avatars/af8d2c02e2ecc39a319a0741aa830422.svg
paper_authors.fullname: Soumya Ghosh
paper_authors.type: user
paper_authors.user: leproquark
paper_authors._id: 6666747494d0f40432a9217f
paper_authors.name: Soham Dan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-10T11:49:21.064Z
paper_authors._id: 65f61702133b39d44f3160f6
paper_authors.avatarUrl: /avatars/320185b4c2ea51818c946fb746b948df.svg
paper_authors.fullname: Soham Dan
paper_authors.type: user
paper_authors.user: soham06
paper_authors._id: 6666747494d0f40432a92180
paper_authors.name: Prasanna Sattigeri
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-10T11:49:27.638Z
paper_authors._id: 63068ec0df993a789e69644b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63068ec0df993a789e69644b/hR4Qs1KFMpF7sW6VzXYH1.png
paper_authors.fullname: Prasanna Sattigeri
paper_authors.type: user
paper_authors.user: pronics2004
paper_title: Large Language Model Confidence Estimation via Black-Box Access
paper_summary: 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.
paper_ai_summary: A framework using engineered features and logistic regression effectively estimates the confidence of large language models with black-box access, outperforming existing methods on multiple datasets.
paper_ai_keywords: large language models black-box access confidence estimation logistic regression Flan-Ul2 LLAMA-13B Mistral-7B TriviaQA SQuAD CoQA Natural Questions AUROC