# Requirements Elicitation Follow-Up Question Generation

arXiv: https://arxiv.org/abs/2507.02858 · HTML: https://mlsanity.com/paper/2507.02858/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 59 references that are in the map.
- First posted on arXiv 3 Jul 2025.

## Builds on (most cited first, 16 of 16)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting](https://mlsanity.com/paper/2310.11324/) (2023)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly](https://mlsanity.com/paper/1911.03343/) (2019)
- [OpenAGI: When LLM Meets Domain Experts](https://mlsanity.com/paper/2304.04370/) (2023)
- [Can LLMs Replace Manual Annotation of Software Engineering Artifacts?](https://mlsanity.com/paper/2408.05534/) (2024)
- [Efficient Continual Pre-training for Building Domain Specific Large Language Models](https://mlsanity.com/paper/2311.08545/) (2023)
- [Elicitron: An LLM Agent-Based Simulation Framework for Design Requirements Elicitation](https://mlsanity.com/paper/2404.16045/) (2024)
- [Designing Effective Interview Chatbots: Automatic Chatbot Profiling and Design Suggestion Generation for Chatbot Debugging](https://mlsanity.com/paper/2104.04842/) (2021)
- [RACER: An LLM-powered Methodology for Scalable Analysis of Semi-structured Mental Health Interviews](https://mlsanity.com/paper/2402.02656/) (2024)
- [The State-of-Practice in Requirements Elicitation: An Extended Interview Study at 12 Companies](https://mlsanity.com/paper/2102.11556/) (2021)
- [How Do Requirements Evolve During Elicitation? An Empirical Study Combining Interviews and App Store Analysis](https://mlsanity.com/paper/2208.00825/) (2022)
- [GPT-Powered Elicitation Interview Script Generator for Requirements Engineering Training](https://mlsanity.com/paper/2406.11439/) (2024)

## Built on by (most cited first, 5 of 5)

- [Can LLMs Generate User Stories and Assess Their Quality?](https://mlsanity.com/paper/2507.15157/) (2025)
- [An Interactive Agent for Requirement-Driven Candidate Sourcing](https://mlsanity.com/paper/2608.23501/) (2026)
- [FeedAIde: Guiding App Users to Submit Rich Feedback Reports by Asking Context-Aware Follow-Up Questions](https://mlsanity.com/paper/2603.04244/) (2026)
- [From Chat to Interview: Agentic Requirements Elicitation with an Experience Ontology](https://mlsanity.com/paper/2605.05828/) (2026)
- [AI-assisted Script Management for Requirements Elicitation Interviews](https://mlsanity.com/paper/2608.01640/) (2026)

## Abstract (arXiv)

Interviews are a widely used technique in eliciting requirements to gather stakeholder needs, preferences, and expectations for a software system. Effective interviewing requires skilled interviewers to formulate appropriate interview questions in real time while facing multiple challenges, including lack of familiarity with the domain, excessive cognitive load, and information overload that hinders how humans process stakeholders' speech. Recently, large language models (LLMs) have exhibited state-of-the-art performance in multiple natural language processing tasks, including text summarization and entailment. To support interviewers, we investigate the application of GPT-4o to generate follow-up interview questions during requirements elicitation by building on a framework of common interviewer mistake types. In addition, we describe methods to generate questions based on interviewee speech. We report a controlled experiment to evaluate LLM-generated and human-authored questions with minimal guidance, and a second controlled experiment to evaluate the LLM-generated questions when generation is guided by interviewer mistake types. Our findings demonstrate that, for both experiments, the LLM-generated questions are no worse than the human-authored questions with respect to clarity, relevancy, and informativeness. In addition, LLM-generated questions outperform human-authored questions when guided by common mistakes types. This highlights the potential of using LLMs to help interviewers improve the quality and ease of requirements elicitation interviews in real time.