Span Based

Span-based methods represent a significant shift in natural language processing, focusing on identifying and utilizing meaningful segments of text (spans) rather than individual tokens. Current research emphasizes improving the consistency and reliability of span predictions, particularly within large language models, and developing novel ensemble techniques to leverage the strengths of multiple models. This approach is proving highly effective in various tasks, including named entity recognition, relation extraction, and question answering, leading to improved accuracy and interpretability in these crucial NLP applications.