This work evaluates prompting strategies for subtask 2 of the GermEval 2025 Harmful Content Detection challenge, which involves classifying whether a tweet attacks the free democratic basic order and shows that techniques such as Chain-of-Thought, In-Context Learning or Task Decomposition outperform approaches like Task Description.
This work studies a variety of retrieval-based in-context learning (RetICL) strategies for detecting defamatory offences under {\S}{\S} 185-187 StGB (the subject of GermEval 2026 Subtask 4).
Kristin Gnadt, Maximilian Meidinger, M. Aßenmacher· 0 citations
The GermEval 2026 shared task scores its detection in four subtasks in a per-subtask nine-voter ensemble spanning three orthogonal axes: LLM, training method and class scope, placing first on all four subtasks.
P. Steigerwald, Eric Rudolph, J. Albrecht· 1 citation
Alation studies reveal a strong synergy between POS features and CRF decoding, with the combined approach yielding the best single-model performance, and ensembling models trained with different objectives improves both overall F1 score and discontinuous MWE scores, demonstrating the importance of training diversity fo...
Yunus Karatepe, Mert Sülük, Zeynep Tu˘gçe Kırımlı et al.· 0 citations
EVIL-Detect, a multi-signal ensemble framework with conflict-aware fusion for NLPCC 2026 Shared Task 6, improves robustness under strong out-of-distribution shifts, achieving a macro-F1 score of 0.8888 and ranking first in the official evaluation.
Hong-Rui Bao, Hangyu Rong, Zhuo Wang et al.· 1 citation
This work finds that likelihood-trained TPMs can result in failed generations due to overly large corrections to the LM’s logits, and trains TPMs with LM-aligned objectives that better align with the LM token-probability space.
Hanzhang Liu, William Zhao, Zi-Lei Shao et al.· 0 citations
This project investigates the identification of candy speech in German YouTube comments as part of the GermEval shared task on Candy Speech Detection, which comprises two subtasks: Coarse-Grained Classification and Fine-Grained Classification.
E. ̧s, Malika Abitova, Fabio Mariani· 0 citations
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