# LLMREI: Automating Requirements Elicitation Interviews with LLMs

arXiv: https://arxiv.org/abs/2507.02564 · HTML: https://mlsanity.com/paper/2507.02564/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2025.

## Built on by (most cited first, 8 of 8)

- [A Research Roadmap for Augmenting Software Engineering Processes and Software Products with Generative AI](https://mlsanity.com/paper/2510.26275/) (2025)
- [An Interactive Agent for Requirement-Driven Candidate Sourcing](https://mlsanity.com/paper/2608.23501/) (2026)
- [Agent4RE: A Self-Refining Multi-agent Framework for End-to-End Software Requirements Engineering and Benchmarking](https://mlsanity.com/paper/2610.10628/) (2026)
- [Automated Alignment between Elicitation Interviews and Requirements](https://mlsanity.com/paper/2510.08622/) (2025)
- [From Chat to Interview: Agentic Requirements Elicitation with an Experience Ontology](https://mlsanity.com/paper/2605.05828/) (2026)
- [How Well Can AI Generate Backlogs from App Mockups?](https://mlsanity.com/paper/2607.22902/) (2026)
- [AI-assisted Script Management for Requirements Elicitation Interviews](https://mlsanity.com/paper/2608.01640/) (2026)
- [A Retrieval-Augmented Automated Stakeholder for Requirements Elicitation Education: A Comparative Study](https://mlsanity.com/paper/2609.12576/) (2026)

## Abstract (arXiv)

Requirements elicitation interviews are crucial for gathering system requirements but heavily depend on skilled analysts, making them resource-intensive, susceptible to human biases, and prone to miscommunication. Recent advancements in Large Language Models present new opportunities for automating parts of this process. This study introduces LLMREI, a chat bot designed to conduct requirements elicitation interviews with minimal human intervention, aiming to reduce common interviewer errors and improve the scalability of requirements elicitation. We explored two main approaches, zero-shot prompting and least-to-most prompting, to optimize LLMREI for requirements elicitation and evaluated its performance in 33 simulated stakeholder interviews. A third approach, fine-tuning, was initially considered but abandoned due to poor performance in preliminary trials. Our study assesses the chat bot's effectiveness in three key areas: minimizing common interview errors, extracting relevant requirements, and adapting its questioning based on interview context and user responses. Our findings indicate that LLMREI makes a similar number of errors compared to human interviewers, is capable of extracting a large portion of requirements, and demonstrates a notable ability to generate highly context-dependent questions. We envision the greatest benefit of LLMREI in automating interviews with a large number of stakeholders.