Annotator disagreement is increasingly recognized as a meaningful property of subjective tasks, rather than merely noise to be removed. Predicting which items are likely to be contested could help allocate annotation effort and route difficult cases to human review. We propose predicting disagreement from interpretable...
Irem Ecem Yelkanat, Faeze Ghorbanpour, Alexander Fraser· 0 citations
This work examines recent byte-level models and their failure to perfectly manipulate characters, finding that the hierarchical design itself limits character-level understanding, with pure byte-level models consistently outperforming hierarchical variants on character manipulation tasks.
It is found that latent intervention improves geometric alignment between languages, and that this improvement translates into consistent gains in cross-lingual consistency with English across both open-ended and multiple-choice QA formats, without degrading factual accuracy.
Faeze Ghorbanpour, Constanza Fierro, Alexander Fraser et al.· 1 citation· ⚡1
By thoroughly unifying 36 English hate speech datasets spanning multiple labeling schemes, this work fine-tune a generalist LLM, based on Qwen3 (Qwen Team, 2025), specifically for hate speech mitigation, demonstrating not only state-of-the-art performance on in-domain benchmarks but also substantial improvements in cro...
Lukas Edman, Daryna Dementieva, Alexander Fraser· 0 citations
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