@inproceedings{hilbert-etal-2026-xplainlp,
    title = "{X}plai{NLP} @ {C}limate{C}heck 2026 Task 2: Comparing Hierarchical Approaches for Fine-Grained Climate Disinformation Narrative Classification",
    author = "Hilbert, Arthur  and
      Yang, Jing  and
      Schmitt, Vera",
    editor = "Rehm, Georg  and
      Dietze, Stefan  and
      Dessi, Danilo  and
      Maynard, Diana  and
      Schimmler, Sonja",
    booktitle = "Proceedings of Natural Scientific Language Processing ({NSLP}) @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/cawl-year/2026.nslp-1.29/",
    doi = "10.63317/4ufq9w234tkf",
    pages = "289--296",
    abstract = "We present our submission to Task{~}2 of the ClimateCheck 2026 shared task on Disinformation Narrative Classification which requires assigning climate-contrarian claims to fine-grained disinformation narratives. Using Qwen3-8B as a fixed backbone, we systematically compare data augmentation, prompt engineering and reinforcement learning techniques. Our experiments show that structured reasoning, particularly a chain-of-thought (CoT) prompting strategy aligned with the taxonomy{'}s hierarchical structure, substantially improves Macro-F1 over both zero-shot baselines and augmentation-based fine-tuning. Our best configuration achieves $\sim$0.625 Macro-F1, ranking first in Task{~}2. Our findings demonstrate that carefully designed hierarchical prompting can rival more complex training interventions in low-resource, highly imbalanced narrative classification settings."
}