LLMs produce stable cognitive distortion labels that improve downstream model performance, paired with a kappa-based framework for dataset-agnostic evaluation in subjective NLP tasks.
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Random forests on string similarity features outperform LLMs for German dialect lexicon induction and boost dialect information retrieval by up to 50% in recall.
HARNESS introduces Arabic-centric speech foundation models that achieve high efficiency and performance through iterative self-distillation and PCA-based signal compression.
citing papers explorer
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Towards Consistent Detection of Cognitive Distortions: LLM-Based Annotation and Dataset-Agnostic Evaluation
LLMs produce stable cognitive distortion labels that improve downstream model performance, paired with a kappa-based framework for dataset-agnostic evaluation in subjective NLP tasks.
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Resource-Lean Lexicon Induction for German Dialects
Random forests on string similarity features outperform LLMs for German dialect lexicon induction and boost dialect information retrieval by up to 50% in recall.
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HARNESS: Lightweight Distilled Arabic Speech Foundation Models
HARNESS introduces Arabic-centric speech foundation models that achieve high efficiency and performance through iterative self-distillation and PCA-based signal compression.