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This paper proposes a gloss-free representation learning approach for cross-dataset sign spotting, using weakly aligned broadcast transcripts in Turkish Sign Language. It shows that LLM-assisted pseudo-gloss normalization improves temporal localization and downstream translation quality.
This paper evaluates cross-dataset generalization of supervised ML/DL models and prompted LLMs for automatic Bloom's taxonomy classification of assessment questions, finding that LLMs are more robust across diverse educational contexts.
This paper proposes a method for cross-table retrieval and alignment of heterogeneous numerical tabular datasets using statistical descriptors and sentence embeddings, enabling similarity matching and interpretable variable-level correspondence without shared column names.