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English(EN) mdok-style at SemEval-2026 Task 9: Finetuning LLMs for Multilingual Polarization Detection

团队利用 LLM 和集成方法进行 SemEval-2026 多语言在线极化检测

作者 PulseAugur 编辑部 · [7 个来源] ·

研究人员为 SemEval-2026 Task 9 开发了系统，这是一项涵盖 22 种语言的多语言极化检测挑战。一种方法使用低秩适配 (LoRA) 微调 Gemma 3 模型，并使用了 GPT-4o-mini 生成的增强数据，取得了 0.811 的平均宏 F1 分数，位列第二。另一种方法侧重于使用 QLoRA 和数据增强技术（如匿名化和同形异义词替换）来微调中型 LLM，以提高鲁棒性。 AI

影响 展示了用于多语言 NLP 任务的高级 LLM 微调技术，有望改进在线内容审核。

排序理由 关于特定 NLP 任务方法的学术论文。

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 7 个来源。 我们如何撰写摘要 →

团队利用 LLM 和集成方法进行 SemEval-2026 多语言在线极化检测

报道来源 [7]

  1. arXiv cs.CL TIER_1 English(EN) · Fengze Guo (University of T\"ubingen), Yue Chang (University of T\"ubingen) ·

    YEZE at SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization via Heterogeneous Ensembling

    arXiv:2605.06231v1 Announce Type: new Abstract: This paper presents our system for SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization, which identifies polarized social media content in 22 languages through three subtasks: binary detecti…

  2. arXiv cs.CL TIER_1 English(EN) · Yue Chang ·

    YEZE at SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization via Heterogeneous Ensembling

    This paper presents our system for SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization, which identifies polarized social media content in 22 languages through three subtasks: binary detection, target classification, and manifestation ide…

  3. arXiv cs.LG TIER_1 English(EN) · Srikar Kashyap Pulipaka ·

    PSK at SemEval-2026 Task 9: Multilingual Polarization Detection Using Ensemble Gemma Models with Synthetic Data Augmentation

    arXiv:2605.05159v1 Announce Type: cross Abstract: We present our system for SemEval-2026 Task 9: Multilingual Polarization Detection, a binary classification task spanning 22 languages. Our approach fine-tunes separate Gemma~3 models (12B and 27B parameters) per language using Lo…

  4. arXiv cs.CL TIER_1 English(EN) · Srikar Kashyap Pulipaka ·

    PSK at SemEval-2026 Task 9: Multilingual Polarization Detection Using Ensemble Gemma Models with Synthetic Data Augmentation

    We present our system for SemEval-2026 Task 9: Multilingual Polarization Detection, a binary classification task spanning 22 languages. Our approach fine-tunes separate Gemma~3 models (12B and 27B parameters) per language using Low-Rank Adaptation (LoRA), augmented with synthetic…

  5. Hugging Face Daily Papers TIER_1 English(EN) ·

    PSK at SemEval-2026 Task 9: Multilingual Polarization Detection Using Ensemble Gemma Models with Synthetic Data Augmentation

    We present our system for SemEval-2026 Task 9: Multilingual Polarization Detection, a binary classification task spanning 22 languages. Our approach fine-tunes separate Gemma~3 models (12B and 27B parameters) per language using Low-Rank Adaptation (LoRA), augmented with synthetic…

  6. arXiv cs.CL TIER_1 English(EN) · Dominik Macko, Alok Debnath, Jakub Simko ·

    mdok-style at SemEval-2026 Task 9: Finetuning LLMs for Multilingual Polarization Detection

    arXiv:2605.02695v1 Announce Type: new Abstract: SemEval-2026 Task 9 is focused on multilingual polarization detection. Specifically, it covers the identification of multilingual, multicultural and multievent polarization along three axes (in subtasks), namely detection, type, and…

  7. arXiv cs.CL TIER_1 English(EN) · Jakub Simko ·

    mdok-style at SemEval-2026 Task 9: Finetuning LLMs for Multilingual Polarization Detection

    SemEval-2026 Task 9 is focused on multilingual polarization detection. Specifically, it covers the identification of multilingual, multicultural and multievent polarization along three axes (in subtasks), namely detection, type, and manifestation. Online polarization presents a c…

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