# Using Large Language Models for Natural Language Processing Tasks in Requirements Engineering: A Systematic Guideline

arXiv: https://arxiv.org/abs/2402.13823 · HTML: https://mlsanity.com/paper/2402.13823/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2024.

## Built on by (most cited first, 6 of 6)

- [ChatNVD: Advancing Cybersecurity Vulnerability Assessment with Large Language Models](https://mlsanity.com/paper/2412.04756/) (2024)
- [Goal2Story: A Multi-Agent Fleet based on Privately Enabled sLLMs for Impacting Mapping on Requirements Elicitation](https://mlsanity.com/paper/2503.13279/) (2025)
- [A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards](https://mlsanity.com/paper/2505.13766/) (2025)
- [From Inductive to Deductive: LLMs-Based Qualitative Data Analysis in Requirements Engineering](https://mlsanity.com/paper/2504.19384/) (2025)
- [AI for Requirements Engineering: Industry adoption and Practitioner perspectives](https://mlsanity.com/paper/2511.01324/) (2025)
- [How Well Can AI Generate Backlogs from App Mockups?](https://mlsanity.com/paper/2607.22902/) (2026)

## Abstract (arXiv)

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.