Learning Conformal Abstention Policies for Adaptive Risk Management in
  Large Language and Vision-Language Models
Large Language and Vision-Language Models (LLMs/VLMs) are increasingly used
in safety-critical applications, yet their opaque decision-making complicates
risk assessment and reliability. Uncertainty quantification (UQ) helps assess
prediction confidence and enables abstention when uncertainty is high.
Conformal prediction (CP), a leading UQ method, provides statistical guarantees
but relies on static thresholds, which fail to adapt to task complexity and
evolving data distributions, leading to suboptimal trade-offs in accuracy,
coverage, and informativeness. To address this, we propose learnable conformal
abstention, integrating reinforcement learning (RL) with CP to optimize
abstention thresholds dynamically. By treating CP thresholds as adaptive
actions, our approach balances multiple objectives, minimizing prediction set
size while maintaining reliable coverage. Extensive evaluations across diverse
LLM/VLM benchmarks show our method outperforms Least Ambiguous Classifiers
(LAC) and Adaptive Prediction Sets (APS), improving accuracy by up to 3.2%,
boosting AUROC for hallucination detection by 22.19%, enhancing
uncertainty-guided selective generation (AUARC) by 21.17%, and reducing
calibration error by 70%-85%. These improvements hold across multiple models
and datasets while consistently meeting the 90% coverage target, establishing
our approach as a more effective and flexible solution for reliable
decision-making in safety-critical applications. The code is available at:
{https://github.com/sinatayebati/vlm-uncertainty}.
67ad315adbc8ae7b3ca9f17d
Sina Tayebati
claimed_verified
2025-02-13T08:22:22.896Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/655ec30b12fb73960ceb048f/q7zVSStJWBywrtPoL2ChO.png
Sina Tayebati
user
sinatayebati
67ad315adbc8ae7b3ca9f17e
Divake Kumar
67ad315adbc8ae7b3ca9f17f
Nastaran Darabi
admin_assigned
2025-02-14T15:29:20.675Z
671acb0de80155d7f9e162b0
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/g7hnS2Mrjyy-RudyIxvVX.png
Nastaran Darabi
user
Nstrndrbi
67ad315adbc8ae7b3ca9f180
Dinithi Jayasuriya
67ad315adbc8ae7b3ca9f181
Ranganath Krishnan
admin_assigned
2025-02-14T15:29:30.994Z
647a45aeccb84c6180b41b54
/avatars/cd0db59a1b7f49f53f65751a8efc1033.svg
Ranganath Krishnan
user
ranganathkrishnan
67ad315adbc8ae7b3ca9f182
Amit Ranjan Trivedi
Learning Conformal Abstention Policies for Adaptive Risk Management in
  Large Language and Vision-Language Models
Large Language and Vision-Language Models (LLMs/VLMs) are increasingly used
in safety-critical applications, yet their opaque decision-making complicates
risk assessment and reliability. Uncertainty quantification (UQ) helps assess
prediction confidence and enables abstention when uncertainty is high.
Conformal prediction (CP), a leading UQ method, provides statistical guarantees
but relies on static thresholds, which fail to adapt to task complexity and
evolving data distributions, leading to suboptimal trade-offs in accuracy,
coverage, and informativeness. To address this, we propose learnable conformal
abstention, integrating reinforcement learning (RL) with CP to optimize
abstention thresholds dynamically. By treating CP thresholds as adaptive
actions, our approach balances multiple objectives, minimizing prediction set
size while maintaining reliable coverage. Extensive evaluations across diverse
LLM/VLM benchmarks show our method outperforms Least Ambiguous Classifiers
(LAC) and Adaptive Prediction Sets (APS), improving accuracy by up to 3.2%,
boosting AUROC for hallucination detection by 22.19%, enhancing
uncertainty-guided selective generation (AUARC) by 21.17%, and reducing
calibration error by 70%-85%. These improvements hold across multiple models
and datasets while consistently meeting the 90% coverage target, establishing
our approach as a more effective and flexible solution for reliable
decision-making in safety-critical applications. The code is available at:
{https://github.com/sinatayebati/vlm-uncertainty}.
Learnable conformal abstention optimizes dynamic thresholds using reinforcement learning to improve uncertainty quantification in large language and vision-language models.
Large Language Models Vision-Language Models uncertainty quantification conformal prediction reinforcement learning adaptive actions prediction set size accuracy coverage informativeness Least Ambiguous Classifiers Adaptive Prediction Sets AUROC AUARC calibration error