Hierarchy-Aware Supervised Uncertainty Estimation for Black-box LLM Taxonomic Reasoning

Shuting Xie; Nathaniel Lesperance; Graham W. Taylor

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arXiv · 2608.22839

Hierarchy-Aware Supervised Uncertainty Estimation for Black-box LLM Taxonomic Reasoning

Shuting Xie·Nathaniel Lesperance·Graham W. Taylor

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Abstract

Large language models (LLMs) are increasingly used for scientific decision support, yet reliable confidence estimation remains difficult in black-box settings. We study uncertainty estimation for hierarchical taxonomic reasoning generated by a black-box LLM in a long-tailed biodiversity monitoring pipeline. Using proxy features extracted by an open-source tool LLM, we train lightweight supervised estimators with hierarchy-aware supervision to predict rank-wise correctness. Across three tool LLMs, the supervised estimators consistently outperform a token-likelihood baseline for micro discrimination and selective prediction under a single global rejection threshold, improving micro AUROC from 0.57 to 0.75--0.80. The best results are achieved by a rank-specific multi-head design (H3), suggesting that accounting for hierarchical output structure is important when a unified abstention rule is required. Our code is publicly available at https://github.com/uoguelph-mlrg/hierarchy-aware-llm-uq

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Shuting Xie, Nathaniel Lesperance, Graham W. Taylor. 2026-08-24. Hierarchy-Aware Supervised Uncertainty Estimation for Black-box LLM Taxonomic Reasoning. https://arxiv.org/abs/2608.22839

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