@inproceedings{jensen-hojmark-2023-formalizing,
    title = "Formalizing content creation and evaluation methods for {AI}-generated social media content",
    author = "Jensen, Christian  and
      H{\o}jmark, Axel",
    editor = "Gehrmann, Sebastian  and
      Wang, Alex  and
      Sedoc, Jo{\~a}o  and
      Clark, Elizabeth  and
      Dhole, Kaustubh  and
      Chandu, Khyathi Raghavi  and
      Santus, Enrico  and
      Sedghamiz, Hooman",
    booktitle = "Proceedings of the Third Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.gem-1.3/",
    pages = "22--41",
    abstract = "This study explores the use of large language models (LLMs), such as ChatGPT and GPT-4, in creating high-quality text-based social media content for businesses on LinkedIn. We introduce a novel architecture incorporating external knowledge bases and a multi-step writing approach, which extracts facts from company websites to form a knowledge graph. Our method{'}s efficacy is assessed using the ``Long-LinkedIn'' evaluation dataset designed for long-form post generation. Results indicate that our iterative refinement significantly improves content quality. However, knowledge-enhanced prompts occasionally reduced quality due to potential formulation issues. LLM-based evaluations, particularly using ChatGPT, showcased potential as a less resource-intensive alternative to human assessments, with a notable alignment between the two evaluation techniques."
}