--- title: "PSK at SemEval-2026 Task 9 — paper by Pulipaka (2026)" description: "This system paper tackles Subtask 1 of SemEval-2026 Task 9. 14 references, cited by 1 paper here." authors: ["Srikar Kashyap Pulipaka"] year: 2026 venue: "Proceedings of the 20th International Workshop on Semantic Evaluation (2026)" source: "https://aclanthology.org/2026.semeval-1.200/" doi: "10.18653/v1/2026.semeval-1.200" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/pulipaka-2026-psk-semeval-2026-task-9/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/pulipaka-2026-psk-semeval-2026-task-9/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/8/) / PSK at SemEval-2026 Task 9: Multilingual Polarization Detection Using Ensemble Gemma Models with Synthetic Data Augmentation # PSK at SemEval-2026 Task 9: Multilingual Polarization Detection Using Ensemble Gemma Models with Synthetic Data Augmentation **Authors:** [Srikar Kashyap Pulipaka](https://md.research.elamur.ai/free/en/authors/srikar-kashyap-pulipaka/) · **Year:** 2026 · **Venue:** [Proceedings of the 20th International Workshop on Semantic Evaluation (2026)](https://md.research.elamur.ai/free/en/venues/semeval/) ([SemEval 2026](https://md.research.elamur.ai/free/en/venues/semeval/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2026.semeval-1.200/) · [PDF](https://aclanthology.org/2026.semeval-1.200.pdf) · [DOI 10.18653/v1/2026.semeval-1.200](https://doi.org/10.18653/v1/2026.semeval-1.200) · [OpenAlex](https://openalex.org/W7166825985) **Citations:** 1 citations (OpenAlex, 2026-10-14) · [1](https://md.research.elamur.ai/free/en/papers/pulipaka-2026-psk-semeval-2026-task-9/cited-by/) in our library, of the 149,439 papers whose bibliographies we read ## Summary This system paper tackles Subtask 1 of SemEval-2026 Task 9. The task asks whether a social media post in one of 22 languages is polarized or not, using the POLAR dataset and macro-F1. Polarization here covers stereotyping, vilification, dehumanization and intolerance. The author trains a separate classifier per language by adapting Gemma 3 (12B and 27B) with LoRA and a classification head. The training sets are small (about 1,700–7,000 examples) and often imbalanced. Each one is enlarged with roughly 1,000 GPT-4o-mini samples, created by native generation, paraphrasing and contrastive minimal pairs, then filtered for label leakage and near-duplicates. Decision thresholds are tuned per language. For each language, the development set picks one of four options: either model alone, a plain average of both, or a weighted mix. The final system reaches 0.811 mean macro-F1, second of 60 teams. The work matters as evidence that robustness across splits counts more than development scores. XLM-RoBERTa and Qwen3 looked competitive in development but collapsed on test. A topic coverage gap in POLAR's Italian data explains that language's weak score. ## Key points - **Leaderboard:** 0.811 versus 0.818 for the winner. The system placed first for Amharic, Hindi and Swahili, top-3 in 8 languages and top-10 in 17. - **Thresholds and ensembles:** - Searching thresholds from 0.3 to 0.7 adds 2–4% F1 with no retraining. - The best values span 0.3 (Nepali) to 0.7 (Khmer, Amharic, Bengali), reflecting miscalibrated probabilities. - Ensembles win 14 of 22 languages. Khmer gains the most (+8.7%). - **Synthetic data:** - A 30% synthetic ratio works best (dev F1 0.822 vs. 0.812 without augmentation). - Khmer loses 6.4% at a 50% ratio. - Deduplication uses paraphrase-multilingual-MiniLM-L12-v2 with a 0.90 cosine cutoff. - **Models and generalization:** - Gemma 3 12B ran in 8-bit on one A100 with LoRA rank 16; Gemma 3 27B ran in 4-bit NF4 on two A100s. - A hybrid XLM-R/Qwen3 submission scored only 0.665. XLM-R fell from 0.856 dev F1 to 0.297 test F1 on Bengali. - **Italian:** It scored worst (0.563). The political and "other" topics never appear in train or dev, yet they make up 41% of the test set and about 87% of its positives. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* 📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works 🧭 Topics (1): [🌍 Multilingual & cross-lingual NLP](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/) · 828 works ## Cite Cite as: Srikar Kashyap Pulipaka. 2026. PSK at SemEval-2026 Task 9: Multilingual Polarization Detection Using Ensemble Gemma Models with Synthetic Data Augmentation. Proceedings of the 20th International Workshop on Semantic Evaluation (2026). The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{pulipaka2026psk, title = {{PSK at SemEval-2026 Task 9: Multilingual Polarization Detection Using Ensemble Gemma Models with Synthetic Data Augmentation}}, author = {Srikar Kashyap Pulipaka}, booktitle = {Proceedings of the 20th International Workshop on Semantic Evaluation (2026)}, year = {2026}, doi = {10.18653/v1/2026.semeval-1.200}, url = {https://doi.org/10.18653/v1/2026.semeval-1.200} } ``` - [← previous Prune as You Generate: Online Rollout Pruning for Faster and Better RLVR](https://md.research.elamur.ai/free/en/papers/xu-2026-prune-as-you-generate-online/) - [next → PsychEthicsBench: Evaluating Large Language Models Against Australian Mental Health Ethics](https://md.research.elamur.ai/free/en/papers/shen-2026-psychethicsbench-evaluating-large-language-models/) - [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/8/) ## References 14 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Jan Cegin, Jakub Simko, and Peter Brusilovsky. 2025. LLMs vs established text augmentation techniques for classification: When do the benefits outweigh the costs? In Proceedings of the 2025 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL). [find](https://scholar.google.com/scholar?q=Jan+Cegin%2C+Jakub+Simko%2C+and+Peter+Brusilovsky%2E+2025%2E+LLMs+vs+established+text+augmentation+techniques+for+classification%3A+When+do+the+benefits+outweigh+the+costs%3F+In+Proceedings+of+the+2025+Conference) 2. Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2023. QLoRA: Efficient finetuning of quantized language models. In Advances in Neural Information Processing Systems, volume 36. 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[LoRA: Low-rank adaptation of large language models](https://md.research.elamur.ai/free/en/papers/hu-2021-lora-low-rank-adaptation-large/). In Proceedings of the Tenth International Conference on Learning Representations (ICLR). 5. Zachary C. Lipton, Charles Elkan, and Balakrishnan Narayanaswamy. 2014. Thresholding classifiers to maximize F1 score. Computing Research Repository, arXiv:1402.1892. — [arXiv:1402.1892](https://arxiv.org/abs/1402.1892) [find](https://scholar.google.com/scholar?q=Zachary+C%2E+Lipton%2C+Charles+Elkan%2C+and+Balakrishnan+Narayanaswamy%2E+2014%2E+Thresholding+classifiers+to+maximize+F1+score%2E+Computing+Research+Repository%2C+arXiv%3A1402%2E1892%2E+%E2%80%94+%5BarXiv%3A1402%2E1892%5D%28https%3A%2F%2Farxiv) 6. Shrirang Mhalgi, Srikar Kashyap Pulipaka, and Sandra Kübler. 2024. IUCL at PAN 2024: Using data augmentation for conspiracy theory detection. In Working Notes of CLEF 2024 – Conference and Labs of the Evaluation Forum, Grenoble, France. 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Gemma Team, Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, Pouya Tafti, Léonard Hussenot, Pier Giuseppe Sessa, Aakanksha Chowdhery, Adam Roberts, Aditya Barua, Alex Botev, Alex Castro-Ros, Ambrose Sl… [find](https://scholar.google.com/scholar?q=Gemma+Team%2C+Thomas+Mesnard%2C+Cassidy+Hardin%2C+Robert+Dadashi%2C+Surya+Bhupatiraju%2C+Shreya+Pathak%2C+Laurent+Sifre%2C+Morgane+Rivi%C3%A8re%2C+Mihir+Sanjay+Kale%2C+Juliette+Love%2C+Pouya+Tafti%2C+L%C3%A9onard+Hussenot%2C+Pier+Gius) 14. Zheng Xin Yong, Cristina Menghini, and Stephen H. Bach. 2024. LexC-Gen: Generating data for extremely low-resource languages with large language models and bilingual lexicons. Computing Research Repository, arXiv:2402.14086. 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