Hierarchical Group-Conditional Conformal Risk Control for Selective Prediction in Language Models
Large language models serve heterogeneous populations structured by domain, topic difficulty, and linguistic style. Conformal risk control (CRC) gives rigorous marginal risk guarantees for selective prediction with abstention, but marginal guarantees do not imply per-group ones: a model can meet the population budget while systematically over-exposing subgroups to errors. Under mild shift in group composition, standard CRC violates the budget in up to 47% of trials. We propose HG-CRC (Hierarchical Group-Conditional CRC), a post-hoc calibration framework enforcing simultaneous risk guarantees across all nodes of a user-defined group hierarchy. It applies a Bonferroni correction over nodes ...
| Authors | Murilo Salem, Luísa Böhm, Daniel Pontes et al. |
|---|---|
| Primary category | cs.AI |
| Published | 27 July 2026 |
| Last updated at source | 27 July 2026 |
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