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Yi Zhao

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SurveyAgent-HKA: A multi-agent framework for scientific survey generation with LLMs and human knowledge augmentation

SurveyAgent-HKA is introduced, a multi-agent framework that improves end-to-end scientific survey generation by incorporating knowledge derived from published surveys and peer-review comments and which outperforms mainstream baselines in citation quality, structural consistency, and content quality.

Tong Bao, Mir Tafseer Nayeem, Yi Zhao et al. · 0 citations
Open access Aug 2026

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 en...

Tong Bao, Yi Zhao, Heng Zhang et al. · 0 citations

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