title: Learning Conformal Abstention Policies for Adaptive Risk Management in
    Large Language and Vision-Language Models
summary: 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}.
paper_authors._id: 67ad315adbc8ae7b3ca9f17d
paper_authors.name: Sina Tayebati
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-13T08:22:22.896Z
paper_authors._id: 655ec30b12fb73960ceb048f
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/655ec30b12fb73960ceb048f/q7zVSStJWBywrtPoL2ChO.png
paper_authors.fullname: Sina Tayebati
paper_authors.type: user
paper_authors.user: sinatayebati
paper_authors._id: 67ad315adbc8ae7b3ca9f17e
paper_authors.name: Divake Kumar
paper_authors._id: 67ad315adbc8ae7b3ca9f17f
paper_authors.name: Nastaran Darabi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-14T15:29:20.675Z
paper_authors._id: 671acb0de80155d7f9e162b0
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/g7hnS2Mrjyy-RudyIxvVX.png
paper_authors.fullname: Nastaran Darabi
paper_authors.type: user
paper_authors.user: Nstrndrbi
paper_authors._id: 67ad315adbc8ae7b3ca9f180
paper_authors.name: Dinithi Jayasuriya
paper_authors._id: 67ad315adbc8ae7b3ca9f181
paper_authors.name: Ranganath Krishnan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-14T15:29:30.994Z
paper_authors._id: 647a45aeccb84c6180b41b54
paper_authors.avatarUrl: /avatars/cd0db59a1b7f49f53f65751a8efc1033.svg
paper_authors.fullname: Ranganath Krishnan
paper_authors.type: user
paper_authors.user: ranganathkrishnan
paper_authors._id: 67ad315adbc8ae7b3ca9f182
paper_authors.name: Amit Ranjan Trivedi
paper_title: Learning Conformal Abstention Policies for Adaptive Risk Management in
    Large Language and Vision-Language Models
paper_summary: 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}.
paper_ai_summary: Learnable conformal abstention optimizes dynamic thresholds using reinforcement learning to improve uncertainty quantification in large language and vision-language models.
paper_ai_keywords: 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