Using Large Language Models for Natural Language Processing Tasks in Requirements Engineering: A Systematic Guideline
<|reference_start|>Using Large Language Models for Natural Language Processing Tasks in Requirements Engineering: A Systematic Guideline: Large Language Models (LLMs) are the cornerstone in automating Requirements Engineering (RE) tasks, underpinning recent advancements in the field. Their pre-trained comprehension of natural language is pivotal for effectively tailoring them to specific RE tasks. However, selecting an appropriate LLM from a myriad of existing architectures and fine-tuning it to address the intricacies of a given task poses a significant challenge for researchers and practitioners in the RE domain. Utilizing LLMs effectively for NLP problems in RE necessitates a dual understanding: firstly, of the inner workings of LLMs, and secondly, of a systematic approach to selecting and adapting LLMs for NLP4RE tasks. This chapter aims to furnish readers with essential knowledge about LLMs in its initial segment. Subsequently, it provides a comprehensive guideline tailored for students, researchers, and practitioners on harnessing LLMs to address their specific objectives. By offering insights into the workings of LLMs and furnishing a practical guide, this chapter contributes towards improving future research and applications leveraging LLMs for solving RE challenges.<|reference_end|>
@article{vogelsang2024using,
    title={Using Large Language Models for Natural Language Processing Tasks in
  Requirements Engineering: A Systematic Guideline},
    author={Andreas Vogelsang, Jannik Fischbach},
    journal={arXiv preprint arXiv:2402.13823},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.13823},
    primaryClass={cs.SE}
}