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

• URL: http://arxiv.org/abs/2401.02575v1
• Date: Thu, 4 Jan 2024 23:37:48 GMT
• Title: Large Language Models for Social Networks: Applications, Challenges, and Solutions
• Authors: Jingying Zeng, Richard Huang, Waleed Malik, Langxuan Yin, Bojan Babic, Danny Shacham, Xiao Yan, Jaewon Yang, Qi He
• Abstract summary: 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.
• Score: 6.6473450630285225
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Federated Large Language Models: Current Progress and Future Directions [63.68614548512534]
  This paper surveys Federated learning for LLMs (FedLLM), highlighting recent advances and future directions. We focus on two key aspects: fine-tuning and prompt learning in a federated setting, discussing existing work and associated research challenges.
  arXiv  Detail & Related papers  (2024-09-24T04:14:33Z)
• Agents in Software Engineering: Survey, Landscape, and Vision [46.021478509599895]
  Large Language Models (LLMs) have achieved remarkable success and have been widely used in various downstream tasks. We find that many studies combining LLMs with software engineering (SE) have employed the concept of agents either explicitly or implicitly. We present a framework of LLM-based agents in SE which includes three key modules: perception, memory, and action.
  arXiv  Detail & Related papers  (2024-09-13T17:55:58Z)
• When Search Engine Services meet Large Language Models: Visions and Challenges [53.32948540004658]
  This paper conducts an in-depth examination of how integrating Large Language Models with search engines can mutually benefit both technologies. We focus on two main areas: using search engines to improve LLMs (Search4LLM) and enhancing search engine functions using LLMs (LLM4Search)
  arXiv  Detail & Related papers  (2024-06-28T03:52:13Z)
• Large Language Models (LLMs) Assisted Wireless Network Deployment in Urban Settings [0.21847754147782888]
  Large Language Models (LLMs) have revolutionized language understanding and human-like text generation. This paper explores new techniques to harness the power of LLMs for 6G (6th Generation) wireless communication technologies. We introduce a novel Reinforcement Learning (RL) based framework that leverages LLMs for network deployment in wireless communications.
  arXiv  Detail & Related papers  (2024-05-22T05:19:51Z)
• Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs [60.40396361115776]
  This paper introduces a novel collaborative approach, namely SlimPLM, that detects missing knowledge in large language models (LLMs) with a slim proxy model. We employ a proxy model which has far fewer parameters, and take its answers as answers. Heuristic answers are then utilized to predict the knowledge required to answer the user question, as well as the known and unknown knowledge within the LLM.
  arXiv  Detail & Related papers  (2024-02-19T11:11:08Z)
• Network Formation and Dynamics Among Multi-LLMs [5.8418144988203915]
  We show that large language models (LLMs) exhibit key social network principles when asked about their preferences in network formation. We also investigate LLMs' decision-making based on real-world networks, revealing that triadic closure and homophily have a stronger influence than preferential attachment.
  arXiv  Detail & Related papers  (2024-02-16T13:10:14Z)
• Knowledge Solver: Teaching LLMs to Search for Domain Knowledge from Knowledge Graphs [19.0797968186656]
  Large language models (LLMs) are versatile and can solve different tasks due to their emergent ability and generalizability. In some previous works, additional modules like graph neural networks (GNNs) are trained on retrieved knowledge from external knowledge bases.
  arXiv  Detail & Related papers  (2023-09-06T15:55:01Z)
• How Can Recommender Systems Benefit from Large Language Models: A Survey [82.06729592294322]
  Large language models (LLM) have shown impressive general intelligence and human-like capabilities. We conduct a comprehensive survey on this research direction from the perspective of the whole pipeline in real-world recommender systems.
  arXiv  Detail & Related papers  (2023-06-09T11:31:50Z)
• Can Large Language Models Transform Computational Social Science? [79.62471267510963]
  Large Language Models (LLMs) are capable of performing many language processing tasks zero-shot (without training data) This work provides a road map for using LLMs as Computational Social Science tools.
  arXiv  Detail & Related papers  (2023-04-12T17:33:28Z)
• Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback [127.75419038610455]
  Large language models (LLMs) are able to generate human-like, fluent responses for many downstream tasks. This paper proposes a LLM-Augmenter system, which augments a black-box LLM with a set of plug-and-play modules.
  arXiv  Detail & Related papers  (2023-02-24T18:48:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.