NoteLLM: A Retrievable Large Language Model for Note Recommendation

Zhang, Chao; Wu, Shiwei; Zhang, Haoxin; Xu, Tong; Gao, Yan; Hu, Yao; Chen, Enhong

doi:10.1145/3589335.3648314

Companion Proceedings of the ACM Web Conference 2024 2024

DOI: 10.1145/3589335.3648314

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NoteLLM: A Retrievable Large Language Model for Note Recommendation

Chao Zhang,

Shiwei Wu,

Haoxin Zhang

et al.

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2024

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Cited by 2 publications

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A survey on large language models for recommendation

Wu,

Zheng,

Qiu

et al. 2024

World Wide Web

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A survey on large language models for recommendation

Wu,

Zheng,

Qiu

et al. 2024

World Wide Web

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Large language models for generative information extraction: a survey

Xu,

Chen,

Peng

et al. 2024

Front. Comput. Sci.

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Information Extraction (IE) aims to extract structural knowledge from plain natural language texts. Recently, generative Large Language Models (LLMs) have demonstrated remarkable capabilities in text understanding and generation. As a result, numerous works have been proposed to integrate LLMs for IE tasks based on a generative paradigm. To conduct a comprehensive systematic review and exploration of LLM efforts for IE tasks, in this study, we survey the most recent advancements in this field. We first present an extensive overview by categorizing these works in terms of various IE subtasks and techniques, and then we empirically analyze the most advanced methods and discover the emerging trend of IE tasks with LLMs. Based on a thorough review conducted, we identify several insights in technique and promising research directions that deserve further exploration in future studies. We maintain a public repository and consistently update related works and resources on GitHub (LLM4IE repository).

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NoteLLM: A Retrievable Large Language Model for Note Recommendation

Cited by 2 publications

References 18 publications

A survey on large language models for recommendation

A survey on large language models for recommendation

Large language models for generative information extraction: a survey

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