@inproceedings{maharjan-etal-2026-feedback,
    title = "Feedback-Aware Prompt Optimization Framework for Generating Job Postings",
    author = "Maharjan, Suraj  and
      Yessenalina, Ainur  and
      Sengamedu, Srinivasan H.",
    editor = {Matusevych, Yevgen  and
      Eryi{\u{g}}it, G{\"u}l{\c{s}}en  and
      Aletras, Nikolaos},
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 5: Industry Track)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-industry.35/",
    pages = "467--474",
    ISBN = "979-8-89176-384-5",
    abstract = "Job postings are critical for recruitment, yet large enterprises struggle with standardization and consistency, requiring significant time from hiring managers and recruiters. We present a feedback-aware prompt optimization framework that automates high-quality job posting generation through iterative human-in-the-loop refinement. Our system integrates multiple data sources: job metadata, competencies, organization{'}s compliance guidelines, and organization brand statement, while incorporating human feedback to continuously improve prompt quality through multi-LLM validation. We evaluated our approach using LLM-as-a-judge on 1,056 job postings and human evaluation on a smaller subset across three dimensions: Standardization, Compliance, and User Perception. Our results demonstrate high compliance rates and strong satisfaction scores in both automated and human evaluation, validating the effectiveness of our feedback-aware approach for enterprise job posting generation."
}