Grigorios Tsoumakas

Quality-aware data selection can significantly enhance the performance of long-document summarization models, outperforming random sampling even at matched training sizes.

Small language models can outperform leading zero-shot LLMs in relation extraction tasks when fine-tuned on task-specific data, challenging the notion that bigger is always better.

Label-Specific Distance-based Oversampling reveals that tailoring synthetic instance generation to label-specific feature relevance can drastically enhance multi-label classification performance.