CzechTopic: A Benchmark for Zero-Shot Topic Localization in Historical Czech Documents
Topic localization aims to identify spans of text that express a given topic defined by a name and description. To study this task, we introduce a human-annotated benchmark based on Czech historical documents, containing human-defined topics together with manually annotated spans and supporting evaluation at both document and word levels. Evaluation is performed relative to human agreement rather than a single reference annotation. We evaluate a diverse range of large language models alongside BERT-based models fine-tuned on a distilled development dataset. Results reveal substantial variability among LLMs, with performance ranging from near-human topic detection to pronounced failures in...
| Authors | Martin Kostelník, Michal Hradiš, Martin Dočekal |
|---|---|
| Primary category | cs.CL |
| Published | 4 March 2026 |
| Last updated at source | 16 September 2026 |
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