Chengzhi Zhang

Enhancing scientific named entity recognition via large language models: a type-driven multi-task learning approach

This work proposes TdSciNER, a type-driven approach that effectively leverages entity type information to enhance SciNER performance and develops a novel demonstration selection strategy based on sentence similarity and entity type diversity to activate the in-context learning capabilities of LLMs, thereby improving entity recognition accuracy across diverse scientific domains.