Learning Conformal Abstention Policies for Adaptive Risk Management in
  Large Language and Vision-Language Models
Authors: name: Sina Tayebati, name: Divake Kumar, name: Nastaran Darabi, name: Dinithi Jayasuriya, name: Ranganath Krishnan, name: Amit Ranjan Trivedi

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}.