Source-linked AI summary
Hierarchy-Aware Supervised Uncertainty Estimation for Black-box LLM Taxonomic Reasoning
Shuting Xie, Nathaniel Lesperance, Graham W. Taylor
TL;DR
Reliable confidence estimation is difficult for black-box LLMs, especially when biodiversity predictions are hierarchical, long-tailed, and partially abstained. The paper trains lightweight estimators from proxy-LLM features and hierarchy-aware supervision, finding that supervised methods outperform a token-likelihood baseline and that rank-specific multi-head outputs perform best overall. The study is limited by sparse fine-rank supervision, a single taxonomic pipeline, and a global abstention threshold.
Problem
Black-box LLMs lack reliable confidence estimates for hierarchical taxonomic reasoning, where long-tailed biodiversity predictions and partial abstentions complicate principled rejection.
Method
The paper trains lightweight supervised estimators using proxy-LLM features, rank-level accuracy and hierarchical F1 supervision, and rank-aware output heads to predict rank-wise correctness.
Results
Supervised estimators outperform a token-likelihood baseline across tool LLMs for micro discrimination and global-threshold selective prediction, with H3 performing best overall.
Takeaways & Limitations
Estimator design that accounts for hierarchical outputs is important when a unified abstention rule is required.
Takeaways & Limitations
The study is limited by sparse fine-rank supervision, a single taxonomic reasoning pipeline, and a global abstention threshold.
Abstract
from arXiv · showhide
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
1 Introduction
The paper addresses confidence estimation for black-box LLMs in hierarchical taxonomic reasoning, motivated by biodiversity monitoring where long-tailed predictions and partial abstentions complicate principled rejection. It formulates rank-level uncertainty estimation and develops hierarchy-aware supervised estimators, with rank-specific multi-head outputs performing best overall.
- Motivation: Black-box LLM confidence estimation remains difficult when hierarchical outputs span multiple semantic levels and abstention produces partial outputs.Reliable uncertainty quantification is especially important in high-stakes decision-support settings.
- Motivation: Long-tailed biodiversity taxonomies make rare and endangered species difficult to monitor, while qualitative abstentions lack numerical confidence for principled rejection thresholds.The motivating pipeline combines VLM image captioning, retrieval-augmented generation, and LLM reasoning across taxonomic ranks.
- Study focus: The study investigates uncertainty estimation for hierarchical taxonomic reasoning using only externally accessible signals from black-box LLMs.This frames the work around uncertainty estimation rather than improving the upstream reasoning pipeline.
- Formulation: The paper casts black-box LLM taxonomic uncertainty estimation as rank-level selective prediction under partial-path abstention.The formulation reflects variable-depth outputs in which the system may abstain at an uncertain rank.
- Method: Lightweight supervised estimators use proxy-LLM features, hierarchy-aware supervision, and rank-aware output parameterizations to predict rank-wise correctness.These design choices align the estimator with the hierarchical structure of the outputs.
- Result: Supervised estimators outperform a token-likelihood baseline for global-threshold selective prediction, with a rank-specific multi-head design performing best overall.The comparison is conducted on a realistic long-tailed arthropod pipeline.
2 Related Work
Prior uncertainty-estimation methods use internal signals, self-verbalized confidence, or repeated-response consistency, while taxonomic prediction adds hierarchical, long-tailed, and partial-output challenges. This paper studies uncertainty estimation for such hierarchical outputs rather than improving the upstream reasoning pipeline.
- LLM uncertainty estimation: White-box uncertainty methods use logits or hidden states, but these signals are unavailable for proprietary API models.Black-box alternatives commonly use self-verbalized confidence or consistency and semantic clustering across sampled responses.
- LLM uncertainty estimation: Self-verbalized confidence may not faithfully reflect intrinsic uncertainty, while repeated-response consistency or semantic clustering can be prohibitively expensive.These constraints motivate uncertainty estimation methods that operate with externally accessible signals.
- Hierarchical taxonomic prediction: Taxonomic reasoning complicates uncertainty estimation because predictions are hierarchical, long-tailed, and often partially specified through abstention.Prior hierarchical-classification work uses ancestor overlap and partial credit, and recent work extends this perspective to taxonomy-aware evaluation.
- Hierarchical taxonomic prediction: The paper complements upstream taxonomic-reasoning work by estimating uncertainty for hierarchical outputs with hierarchy-aware signals as auxiliary supervision.Its focus is uncertainty estimation rather than improving the upstream reasoning pipeline.
3 Methodology
The study formulates black-box taxonomic uncertainty estimation as rank-level selective prediction and trains lightweight estimators from tool-LLM proxy features with hierarchy-aware supervision. It evaluates rank-wise correctness using AUROC and global-threshold risk–coverage metrics.
- Pipeline: The pipeline maps target-LLM taxonomic outputs and tool-LLM proxy features to confidence scores for uncertainty estimation.
- Supervision labels: Each instance receives binary correctness supervision and hierarchical accuracy based on taxonomic overlap, with constant kingdom and phylum ranks excluded.
- Proxy features: Tool LLMs provide representation features from hidden states and distributional features based on entropy, negative log-probability, and token probability.
- Supervised estimators: H3 uses five rank-specific binary heads plus an auxiliary regression head, whereas H1 and H2 use shared binary-head designs with progressively richer supervision.
- Evaluation protocol: Rank-level evaluation treats each image–rank pair as one example with a correctness label and confidence score.
- Evaluation protocol: Global-threshold risk–coverage pools validation examples across ranks, while AUROC is reported as both pooled micro AUROC and per-rank-average macro AUROC.
4 Results
Supervised uncertainty estimators outperform the mean-NLL baseline across tool LLMs for micro discrimination and global-threshold selective prediction. H3 provides the strongest overall risk–coverage trade-off, while gains are less consistent under macro AUROC.
- Supervised estimators consistently and substantially outperform the mean-NLL baseline in micro AUROC across all three tool LLMs.
- H3 achieves the highest mean micro AUROC overall, with a small margin over H1 for Qwen3 and clearer gains for Gemma 7B and GPT-OSS 20B.
- Under a single global rejection threshold, supervised estimators achieve lower selective risk than the mean-NLL baseline across a broad coverage range.The largest gains occur at low-to-mid coverage.
- H3 consistently yields the best overall risk–coverage trade-off, while H1 and H2 remain better than the baseline throughout.
- Gains are less consistent under rank-balanced macro AUROC, although calibration results generally favour supervised estimators over the mean-NLL baseline.
5 Discussion
Micro and macro AUROC diverge because rank imbalance favors coarse, high-coverage ranks, while deeper ranks have weaker training signals. For unified-threshold deployment, H3’s rank-specific heads perform best, but sparse fine-rank supervision and limited evaluation scope constrain the conclusions.
- Rank imbalance makes micro AUROC favor high-coverage coarse ranks, whereas macro AUROC weights ranks equally and exposes weaknesses at deeper ranks.Frequent abstentions reduce fine-rank training signal and increase variance.
- H3 performs best overall for unified-threshold deployment, while H2 provides modest gains over H1 for most tool LLMs except GPT-OSS 20B.
- Because H2 and H3 share auxiliary supervision, their pattern suggests rank-specific output heads matter more than the auxiliary hierarchical target alone.
- The study is limited by sparse fine-rank supervision, a single taxonomic reasoning pipeline, and a global abstention threshold.Future work should test richer fine-rank coverage, wider distribution shifts, and rank-specific decision rules.
6 Conclusion
The paper studies uncertainty estimation for black-box LLMs performing hierarchical taxonomic reasoning under long-tailed data and partial-path abstention. Lightweight supervised estimators using proxy features outperform an NLL baseline, with rank-specific multi-head output performing best overall.
- The study addresses UQ for black-box LLMs in hierarchical taxonomic reasoning with long-tailed data and partial-path abstention.
- Lightweight supervised estimators use proxy features from a tool LLM and hierarchy-aware signals to predict rank-wise correctness.
- Across tool LLMs, supervised estimators outperform an NLL baseline in micro discrimination and global-threshold selective prediction.
- A rank-specific multi-head design performs best overall, highlighting estimator design for hierarchical outputs in unified-threshold deployment.
A Implementation details
The implementation trains lightweight MLP estimators with Adam, early stopping, hierarchical supervision, and an L1 penalty encouraging sparse input-feature use. Feature selection is applied only to hidden-state representation features.
- MLPs use Adam with learning rate 10^-3, weight decay 3 × 10^-4, batch size 128, and up to 200 epochs.
- Early stopping monitors validation micro AUROC with patience 5 and minimum improvement 10^-4.
- Hierarchical supervision uses λ = 0.1, while an L1 penalty of 10^-4 encourages sparse input-to-hidden weights.
- Feature selection is applied only to hidden-state representation features, combining the top-100 features from each listed selection method.
B Upstream taxonomic pipeline statistics
Table 2 reports rank coverage and prediction accuracy in the upstream system before filtering.
- Table 2 reports rank coverage and prediction accuracy before filtering.
C Tool LLM configurations and extracted features
The paper describes tool LLM configurations and feature types used for uncertainty estimation, including representation and distributional features.
- Table 3 lists each tool LLM and its exact checkpoint identifier.
- Tool LLMs extract representation, distribution, and hybrid feature types.
D Additional UQ results
Additional results cover macro AUROC, per-rank AUROC, and calibration before and after temperature scaling.
- Table 4 reports all-rank aggregated macro AUROC across tool LLMs.Results are reported as mean ± std over 5 random seeds.
- Figure 4 reports per-rank AUROC before calibration across 5 random seeds.Horizontal bars indicate ±1 standard deviation.
- Table 5 reports calibration metrics before and after temperature scaling.Entries are mean with standard deviation across seeds, and arrows indicate whether lower or higher values are preferred.