Large Language Model Confidence Estimation via Black-Box Access
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.
6666747494d0f40432a9217c
Tejaswini Pedapati
admin_assigned
2024-06-10T11:48:14.348Z
663272af2a4742700eb9fc4e
/avatars/aaa988209a7a7b1607885247da399a9a.svg
Tejaswini Pedapati
user
tejaswinipedapati
6666747494d0f40432a9217d
Amit Dhurandhar
admin_assigned
2024-06-10T11:48:20.667Z
65fc84fe49382fd61f828862
/avatars/80fe08a14493e2a83464de8d9aab010b.svg
Amit Dhurandhar
user
sadhamanus
6666747494d0f40432a9217e
Soumya Ghosh
admin_assigned
2024-06-10T11:49:14.384Z
65d11bc5bf849f3b79571771
/avatars/af8d2c02e2ecc39a319a0741aa830422.svg
Soumya Ghosh
user
leproquark
6666747494d0f40432a9217f
Soham Dan
admin_assigned
2024-06-10T11:49:21.064Z
65f61702133b39d44f3160f6
/avatars/320185b4c2ea51818c946fb746b948df.svg
Soham Dan
user
soham06
6666747494d0f40432a92180
Prasanna Sattigeri
admin_assigned
2024-06-10T11:49:27.638Z
63068ec0df993a789e69644b
https://cdn-avatars.huggingface.co/v1/production/uploads/63068ec0df993a789e69644b/hR4Qs1KFMpF7sW6VzXYH1.png
Prasanna Sattigeri
user
pronics2004
Large Language Model Confidence Estimation via Black-Box Access
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.
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.
large language models black-box access confidence estimation logistic regression Flan-Ul2 LLAMA-13B Mistral-7B TriviaQA SQuAD CoQA Natural Questions AUROC