@inproceedings{souza-etal-2025-generative,
    title = "Generative {AI} for Technical Writing: {Comparing} Human and {LLM} Assessments of Generated Content",
    author = "Souza, Karen de  and
      Nikolaev, Alexandre  and
      Koponen, Maarit",
    editor = "Johansson, Richard  and
      Stymne, Sara",
    booktitle = "Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)",
    month = mar,
    year = "2025",
    address = "Tallinn, Estonia",
    publisher = "University of Tartu Library",
    url = "https://preview.aclanthology.org/corrections-2025-07/2025.nodalida-1.67/",
    pages = "661--679",
    ISBN = "978-9908-53-109-0",
    abstract = "Large language models (LLMs) have recently gained significant attention for their capabilities in natural language processing (NLP), particularly generative artificial intelligence (AI). LLMs can also be useful tools for software documentation technical writers. We present an assessment of technical documentation content generated by three different LLMs using retrieval-augmented technology (RAG) with product documentation as a knowledge base. The LLM-generated responses were analyzed in three ways: 1) manual error analysis by a technical writer, 2) automatic assessment using deterministic metrics (BLEU, ROUGE, token overlap), and 3) evaluation of correctness by LLM as a judge. The results of these assessments were compared using a Network Analysis and linear regression models to investigate statistical relationships, model preferences, and the distribution of human and LLM scores. The analyses concluded that human quality evaluation is more related to the LLM correctness judgment than deterministic metrics, even when using different analysis frameworks."
}