LOFT unifies orthogonal PEFT by treating adaptation as low-rank subspace rotation and adds task-aware support selection that improves efficiency under fixed budgets.
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3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
LLMs fail at extended counting of repeated characters due to finite internal states, with abrupt errors persisting across model scales and inference methods.
An ensemble of per-language fine-tuned Gemma 3 models with three synthetic data strategies and per-language threshold tuning achieves 2nd place overall in SemEval-2026 Task 9 with mean macro-F1 of 0.811.
citing papers explorer
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LOFT: Low-Rank Orthogonal Fine-Tuning via Task-Aware Support Selection
LOFT unifies orthogonal PEFT by treating adaptation as low-rank subspace rotation and adds task-aware support selection that improves efficiency under fixed budgets.
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Language models fail at extended rule following
LLMs fail at extended counting of repeated characters due to finite internal states, with abrupt errors persisting across model scales and inference methods.
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PSK at SemEval-2026 Task 9: Multilingual Polarization Detection Using Ensemble Gemma Models with Synthetic Data Augmentation
An ensemble of per-language fine-tuned Gemma 3 models with three synthetic data strategies and per-language threshold tuning achieves 2nd place overall in SemEval-2026 Task 9 with mean macro-F1 of 0.811.