Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification
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Computer Science > Computation and Language
Title:Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification
Abstract:Confidence estimation is essential when LLMs are used for classification, indicating when predictions can be trusted. However, common approaches such as verbalization produce extremely sparse outputs. For instance, Qwen3-32B verbalizes only eight unique confidence values on SST-2, with over half being exactly 95%, a pattern we observe consistently across four datasets and two LLMs. Besides limiting practical utility, we show that this sparsity critically affects evaluation: the choice of interpolation in area under the accuracy-rejection curve (AUARC) dramatically alters rankings, with consistency sampling dropping from best to worst under stepwise versus linear interpolation. We advocate for standardizing stepwise interpolation for a fairer comparison. Under such a fair evaluation, we find that weighting verbalized digits by token probabilities, a method we term verbalization logprobs, addresses sparsity and achieves the best AUARC (+2.3 points over vanilla verbalization) without incurring additional inference cost.
| Comments: | Published at Findings of ACL 2026 |
| Subjects: | Computation and Language (cs.CL) |
| ACM classes: | I.2.7 |
| Cite as: | arXiv:2608.04899 [cs.CL] |
| (or arXiv:2608.04899v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.04899
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | Findings of the Association for Computational Linguistics: ACL 2026, pages 33424-33435 |
| Related DOI: | https://doi.org/10.18653/v1/2026.findings-acl.1671
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