@inproceedings{ebrahim-etal-2025-enhancing,
    title = "Enhancing Software Requirements Engineering with Language Models and Prompting Techniques: Insights from the Current Research and Future Directions",
    author = "Ebrahim, Moemen  and
      Guirguis, Shawkat  and
      Basta, Christine",
    editor = "Zhao, Jin  and
      Wang, Mingyang  and
      Liu, Zhu",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.acl-srw.31/",
    pages = "486--496",
    ISBN = "979-8-89176-254-1",
    abstract = "Large Language Models (LLMs) offer transformative potential for Software Requirements Engineering (SRE), yet critical challenges, including domain ignorance, hallucinations, and high computational costs, hinder their adoption. This paper proposes a conceptual framework that integrates Small Language Models (SLMs) and Knowledge-Augmented LMs (KALMs) with LangChain to address these limitations systematically. Our approach combines: (1) SLMs for efficient, locally deployable requirements processing, (2) KALMs enhanced with Retrieval-Augmented Generation (RAG) to mitigate domain-specific gaps, and (3) LangChain for structured, secure workflow orchestration. We identify and categorize six technical challenges and two research gaps through a systematic review of LLM applications in SRE. To guide practitioners, we distill evidence-based prompt engineering guidelines (Context, Language, Examples, Keywords) and propose prompting strategies (e.g., Chain-of-Verification) to improve output reliability. The paper establishes a theoretical foundation for scalable, trustworthy AI-assisted SRE and outlines future directions, including domain-specific prompt templates and hybrid validation pipelines."
}