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--- |
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library_name: peft |
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base_model: meta-llama/Llama-2-7b-hf |
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language: |
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- en |
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pipeline_tag: text-generation |
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tags: |
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- hate-speech |
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- explanation-generation |
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--- |
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# Model Card for gllama-alarm-implicit-hate |
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**GLlama Alarm** is a suite of knowledge-Guided versions of Llama 2 instruction fine-tuned for non-binary abusive language detection and explanation generation tasks. |
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## Model Details |
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This version has been instruction fine-tuned on Implicit Hate Corpus for multi-class expressiveness detection and explanation generation (i.e., implicit hate speech, explicit hate speech, not hate) as well as on encyclopedic, commonsense and temporal linguistic knowledge. |
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### Model Description |
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- **Developed by:** Chiara Di Bonaventura, Lucia Siciliani, Pierpaolo Basile |
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- **Funded by:** The Alan Turing Institute, Fondazione FAIR |
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- **Language:** English |
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- **Finetuned from model:** meta-llama/Llama-2-7b-hf |
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### Model Sources |
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- **Paper:** https://kclpure.kcl.ac.uk/ws/portalfiles/portal/316198577/2025_COLING_from_detection_to_explanation.pdf |
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## Uses |
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**GLlama Alarm** is intended for research use in English, especially for NLP tasks in the domain of social media, which might contain offensive content. |
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Our suite can be used to **detect different levels of offensiveness and expressiveness of abusive language** (e.g. offensive comments, implicit hate speech, which has proven to be hard for many LLMs) and to **generate structured textual explanations** entailing why the text contains abusive language. |
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In any case, language models, including ours, can potentially be used for language generation in a harmful way. GLlama Alarm should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application. |
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## Training Details |
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**GLlama Alarm** builds on top of the foundational model Llama 2 (7B), which is an auto-regressive language model that uses an optimized transformer architecture. |
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Llama 2 was trained on a mix of publicly available online data between January 2023 and July 2023. We select the base version of Llama 2, which has 7B parameters. |
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We instruction-funed Llama 2 on the following datasets: HateXplain and Implicit Hate Corpus, separately. This version is the one instruction fine-tuned on Implicit Hate Corpus. |
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These datasets contain publicly available data designed for hate speech detection, thus ensuring data privacy and protection. |
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To instruction fine-tune Llama 2, we created knowledge-guided prompts following our paradigm. The template is shown in Table 9 of the paper. |
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We instruction fine-tuned Llama 2 with 17k knowledge-guided prompts for HateXplain and Implicit Hate for 5 epochs, while setting the other parameters as suggested by [Taori et al., 2023](https://github.com/tatsu-lab/stanford_alpaca). |
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## Citation |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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@inproceedings{dibonaventura2025gllama_alarm, |
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title={From Detection to Explanation: Effective Learning Strategies for LLMs in Online Abusive Language Research}, |
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author={Di Bonaventura, Chiara and Siciliani, Lucia and Basile, Pierpaolo and Merono-Penuela, Albert and McGillivray, Barbara}, |
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booktitle={Proceedings of the 2025 International Conference on Computational Linguistics (COLING 2025)}, |
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year={2025} |
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} |
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**APA:** |
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Di Bonaventura, C., Siciliani, L., Basile, P., Merono-Penuela, A., & McGillivray, B. 2025. From Detection to Explanation: Effective Learning Strategies for LLMs in Online Abusive Language Research. |
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In Proceedings of the 2025 International Conference on Computational Linguistics (COLING 2025). |
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## Model Card Contact |
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chiara.di[email protected] |
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### Framework versions |
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- PEFT 0.10.0 |
