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        <title>NLPDame at ClimateActivism 2024: Mistral Sequence Classification with PEFT for Hate Speech, Targets and Stance Event Detection</title>
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            <title>Proceedings of the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2024)</title>
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        <name type="personal">
            <namePart type="given">Ali</namePart>
            <namePart type="family">Hürriyetoğlu</namePart>
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        <name type="personal">
            <namePart type="given">Hristo</namePart>
            <namePart type="family">Tanev</namePart>
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        <name type="personal">
            <namePart type="given">Gökçe</namePart>
            <namePart type="family">Uludoğan</namePart>
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    <abstract>The paper presents the approach developed for the “Climate Activism Stance and Hate Event Detection” Shared Task at CASE 2024, comprising three sub-tasks. The Shared Task aimed to create a system capable of detecting hate speech, identifying the targets of hate speech, and determining the stance regarding climate change activism events in English tweets. The approach involved data cleaning and pre-processing, addressing data imbalance, and fine-tuning the “mistralai/Mistral-7B-v0.1” LLM for sequence classification using PEFT (Parameter-Efficient Fine-Tuning). The LLM was fine-tuned using two PEFT methods, namely LoRA and prompt tuning, for each sub-task, resulting in the development of six Mistral-7B fine-tuned models in total. Although both methods surpassed the baseline model scores of the task organizers, the prompt tuning method yielded the highest results. Specifically, the prompt tuning method achieved a Macro-F1 score of 0.8649, 0.6106 and 0.6930 in the test data of sub-tasks A, B and C, respectively.</abstract>
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%0 Conference Proceedings
%T NLPDame at ClimateActivism 2024: Mistral Sequence Classification with PEFT for Hate Speech, Targets and Stance Event Detection
%A Christodoulou, Christina
%Y Hürriyetoğlu, Ali
%Y Tanev, Hristo
%Y Thapa, Surendrabikram
%Y Uludoğan, Gökçe
%S Proceedings of the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F christodoulou-2024-nlpdame
%X The paper presents the approach developed for the “Climate Activism Stance and Hate Event Detection” Shared Task at CASE 2024, comprising three sub-tasks. The Shared Task aimed to create a system capable of detecting hate speech, identifying the targets of hate speech, and determining the stance regarding climate change activism events in English tweets. The approach involved data cleaning and pre-processing, addressing data imbalance, and fine-tuning the “mistralai/Mistral-7B-v0.1” LLM for sequence classification using PEFT (Parameter-Efficient Fine-Tuning). The LLM was fine-tuned using two PEFT methods, namely LoRA and prompt tuning, for each sub-task, resulting in the development of six Mistral-7B fine-tuned models in total. Although both methods surpassed the baseline model scores of the task organizers, the prompt tuning method yielded the highest results. Specifically, the prompt tuning method achieved a Macro-F1 score of 0.8649, 0.6106 and 0.6930 in the test data of sub-tasks A, B and C, respectively.
%U https://aclanthology.org/2024.case-1.13
%P 96-104

Markdown (Informal)

[NLPDame at ClimateActivism 2024: Mistral Sequence Classification with PEFT for Hate Speech, Targets and Stance Event Detection](https://aclanthology.org/2024.case-1.13) (Christodoulou, CASE-WS 2024)

• NLPDame at ClimateActivism 2024: Mistral Sequence Classification with PEFT for Hate Speech, Targets and Stance Event Detection (Christodoulou, CASE-WS 2024)

ACL

• Christina Christodoulou. 2024. NLPDame at ClimateActivism 2024: Mistral Sequence Classification with PEFT for Hate Speech, Targets and Stance Event Detection. In Proceedings of the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2024), pages 96–104, St. Julians, Malta. Association for Computational Linguistics.