LLM + Citation = Better Knowledge Mining

Let’s assume a collection of academic papers and a group of researchers. The citation recommendation model assesses the potential advantage a distinct paper from this collection may offer to a specific researcher. This assessment is conducted through a utility function, designated to quantify the benefit. Essentially, this function ascertains the degree of alignment between a paper and a researcher’s requirements or inclinations.

A recent paper by Yang Zhang et al. (DOI: 10.48550/arXiv.2309.09727) summarises existing research on how Large Language Models (LLMs) contribute to citation-related tasks. The picture in this post represents their conceptual framework. There are exciting opportunities in this space for new developments.

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Reference: When Large Language Models Meet Citation: A Survey
By: Yang Zhang, Yufei Wang, Kai Wang, Quan Z. Sheng, Lina Yao, Adnan Mahmood, Wei Emma Zhang, Rongying Zhao
Journal/Conference: arxiv.org
DOI: https://doi.org/10.48550/arXiv.2309.09727
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