Large Language Models for Social Networks: Applications, Challenges, and Solutions

Jingying Zeng, Richard Huang, Waleed Malik, Langxuan Yin, Bojan Babic +4 more
1/4/2024
cs.SIcs.AIcs.LG

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Abstract

Large Language Models (LLMs) are transforming the way people generate, explore, and engage with content. We study how we can develop LLM applications for online social networks. Despite LLMs' successes in other domains, it is challenging to develop LLM-based products for social networks for numerous reasons, and it has been relatively under-reported in the research community. We categorize LLM applications for social networks into three categories. First is knowledge tasks where users want to find new knowledge and information, such as search and question-answering. Second is entertainment tasks where users want to consume interesting content, such as getting entertaining notification content. Third is foundational tasks that need to be done to moderate and operate the social networks, such as content annotation and LLM monitoring. For each task, we share the challenges we found, solutions we developed, and lessons we learned. To the best of our knowledge, this is the first comprehensive paper about developing LLM applications for social networks.

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Cite this paper

@article{zeng2024large,
  title  = {Large Language Models for Social Networks: Applications, Challenges, and Solutions},
  author = {Jingying Zeng and Richard Huang and Waleed Malik and Langxuan Yin and Bojan Babic and Danny Shacham and Xiao Yan and Jaewon Yang and Qi He},
  year   = {2024},
  eprint = {2401.02575},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2401.02575v1}
}

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