IGGQ Research Publishing
Machine Intelligence & Responsible Systems

Calibrated Abstention and Escalation in Foundation-Model Systems

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Abstract

This review examines calibrated abstention and escalation in foundation-model systems. The organizing question is when a model should answer, defer, request evidence, or transfer control to a person. Ten related scholarly sources are synthesized through a decision-centered framework spanning problem definition, mechanism, measurement, evaluation, implementation, and governance. The review does not invent experiments, pooled estimates, or unreported quantitative results. It instead evaluates the strength and transferability of the available evidence, with particular attention to treating a fluent refusal style as evidence of calibrated uncertainty. The resulting framework links technical or empirical performance to explicit use conditions and identifies tests that should precede wider adoption in high-consequence decision support with bounded model authority.

Keywords
large language modelsabstentioncalibrationuncertaintyhuman escalation
References
  1. Ehrlich-Sommer, F., Eberhard, B., & Holzinger, A. (2025). ForestGPT and Beyond: A Trustworthy Domain-Specific Large Language Model Paving the Way to Forestry 5.0. Electronics, 14(18), 3583. https://doi.org/10.3390/electronics14183583 DOI
  2. Geng, J., Cai, F., Wang, Y., Koeppl, H., Nakov, P., & Gurevych, I. (2023). A Survey of Confidence Estimation and Calibration in Large Language Models. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2311.08298 DOI
  3. Hu, M., Zhang, Z., Zhao, S., Huang, M., & Wu, B. (2023). Uncertainty in Natural Language Processing: Sources, Quantification, and Applications. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2306.04459 DOI
  4. Lin, Z., Trivedi, S., & Sun, J. (2023). Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2305.19187 DOI
  5. Lu, Y., Chen, T., Hao, N., Rechem, C. V., Chen, J., & Fu, T. (2024). Uncertainty Quantification and Interpretability for Clinical Trial Approval Prediction. Health Data Science, 4, 0126. https://doi.org/10.34133/hds.0126 DOI
  6. Mena, J., Pujol, O., & Vitria, J. (2020). Uncertainty-Based Rejection Wrappers for Black-Box Classifiers. IEEE Access, 8, 101721-101746. https://doi.org/10.1109/access.2020.2996495 DOI
  7. Ren, J., Zhao, Y., Vu, T., Liu, P. J., & Lakshminarayanan, B. (2023). Self-Evaluation Improves Selective Generation in Large Language Models. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2312.09300 DOI
  8. Shorinwa, O., Mei, Z., Lidard, J., Ren, A. Z., & Majumdar, A. (2025). A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions. ACM Computing Surveys, 58(3), 1-38. https://doi.org/10.1145/3744238 DOI
  9. Tomani, C., Chaudhuri, K., Evtimov, I., Cremers, D., & Ibrahim, M. (2024). Uncertainty-Based Abstention in LLMs Improves Safety and Reduces Hallucinations. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2404.10960 DOI
  10. Wen, B., Yao, J., Feng, S., Xu, C., Tsvetkov, Y., Howe, B., & Wang, L. L. (2025). Know Your Limits: A Survey of Abstention in Large Language Models. Transactions of the Association for Computational Linguistics, 13, 529-556. https://doi.org/10.1162/tacl_a_00754 DOI
Publication details
Journal
Machine Intelligence & Responsible Systems
Volume
1 (2026)
Article number
mi20260001
License
CC BY 4.0