Papers by Elena Merdjanovska
Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification (2026.findings-acl)
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| Challenge: | Xuan et al., 2023) show that verbalization produces extremely sparse outputs for confidence estimation. |
| Approach: | They propose to standardize stepwise interpolation for a fairer comparison . they advocate standardizing stepwise intercepts for AUARC evaluation . |
| Outcome: | The proposed method achieves the best AUARC score (+2.3 points over vanilla verbalization) while requiring less inference cost. |
NoiseBench: Benchmarking the Impact of Real Label Noise on Named Entity Recognition (2024.emnlp-main)
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| Challenge: | Existing approaches to named entity recognition often contain a significant percentage of incorrect labels for entity types and boundary boundaries. |
| Approach: | They propose a noise-robust learning approach that learns from data with partially incorrect labels. |
| Outcome: | The proposed methods are based on simulated noise and are easier to handle than simulated real noise caused by human error or semi-automatic annotation. |
Token-Level Metrics for Detecting Incorrect Gold Annotations in Named Entity Recognition (2025.findings-emnlp)
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| Challenge: | Annotated datasets for supervised learning often contain incorrect labels, i.e. label noise. |
| Approach: | They compare popular sample metrics for detecting incorrect annotations in named entity recognition (NER) they find that training dynamics metrics work the best overall, and they detect errors that the model has not yet memorized . |
| Outcome: | The proposed measures reduce label noise across noise types by detecting errors in trained models. |