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Open-weight LLMs show promise for Danish offensive language detection

By PulseAugur Editorial · [1 sources] ·

A new arXiv paper investigates the performance of open-weight large language models (LLMs) like Llama, Mistral, Gemma, and Qwen for detecting offensive language in Danish, a low-resource task. The study found that even without specific fine-tuning, these LLMs outperformed fine-tuned BERT-family models. While prompt design and model pre-training interact in complex ways, semantically grounded prompts proved more robust than format-constrained ones. The research also highlighted that Danish-specific characters like 'æ', 'ø', and 'å' carry meaningful signal, and that class imbalance remains a significant challenge for accurate detection. AI

IMPACT Demonstrates the potential of open-weight LLMs for specialized, low-resource NLP tasks, suggesting broader applicability beyond high-resource languages.

RANK_REASON Academic paper detailing model performance on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Open-weight LLMs show promise for Danish offensive language detection

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Academic paper detailing model performance on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Elnur Adl Zarabi, Amit Kumar, Adi Singh, Sierra Nelson, Megan Stubbs-Richardson, Taoran Ji ·

    On the Effectiveness and Limitations of Open-Weight LLMs for Low-Resource Offensive Language Detection in Danish

    arXiv:2611.09506v1 Announce Type: new Abstract: We study the effectiveness and robustness of open-weight large language models (LLMs) for Danish offensive language detection, an underexplored low-resource task. We evaluate four model families (Llama, Mistral, Gemma, and Qwen) acr…

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