# Elicitron: An LLM Agent-Based Simulation Framework for Design Requirements Elicitation

arXiv: https://arxiv.org/abs/2404.16045 · HTML: https://mlsanity.com/paper/2404.16045/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 11 of its 53 references that are in the map.
- First posted on arXiv 4 Apr 2024.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [Role-Play with Large Language Models](https://mlsanity.com/paper/2305.16367/) (2023)
- [Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design](https://mlsanity.com/paper/2302.02913/) (2023)
- [Conceptual Design Generation Using Large Language Models](https://mlsanity.com/paper/2306.01779/) (2023)
- [ShipGen: A Diffusion Model for Parametric Ship Hull Generation with Multiple Objectives and Constraints](https://mlsanity.com/paper/2311.06315/) (2023)
- [Toward Artificial Empathy for Human-Centered Design: A Framework](https://mlsanity.com/paper/2303.10583/) (2023)
- [DATED: Guidelines for Creating Synthetic Datasets for Engineering Design Applications](https://mlsanity.com/paper/2305.09018/) (2023)
- [On the Noise Scheduling for Generating Plausible Designs with Diffusion Models](https://mlsanity.com/paper/2311.11207/) (2023)

## Built on by (most cited first, 19 of 19)

- [Large Language Model-Based Agents for Software Engineering: A Survey](https://mlsanity.com/paper/2409.02977/) (2024)
- [LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Ahead](https://mlsanity.com/paper/2404.04834/) (2024)
- [From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future](https://mlsanity.com/paper/2408.02479/) (2024)
- [From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents](https://mlsanity.com/paper/2412.03563/) (2024)
- [Very Large-Scale Multi-Agent Simulation in AgentScope](https://mlsanity.com/paper/2407.17789/) (2024)
- [An LLM-based multi-agent framework for agile effort estimation](https://mlsanity.com/paper/2509.14483/) (2025)
- [Requirements Elicitation Follow-Up Question Generation](https://mlsanity.com/paper/2507.02858/) (2025)
- [Deep Generative Model for Mechanical System Configuration Design](https://mlsanity.com/paper/2409.06016/) (2024)
- [Simulation Agent: A Framework for Integrating Simulation and Large Language Models for Enhanced Decision-Making](https://mlsanity.com/paper/2505.13761/) (2025)
- [Requirements Development and Formalization for Reliable Code Generation: A Multi-Agent Vision](https://mlsanity.com/paper/2508.18675/) (2025)
- [Towards Engineering Multi-Agent LLMs: A Protocol-Driven Approach](https://mlsanity.com/paper/2510.12120/) (2025)
- [Issue-Oriented Agent-Based Framework for Automated Review Comment Generation](https://mlsanity.com/paper/2511.00517/) (2025)
- [Can LLMs Generate User Stories and Assess Their Quality?](https://mlsanity.com/paper/2507.15157/) (2025)
- [Getting Inspiration for Feature Elicitation: App Store- vs. LLM-based Approach](https://mlsanity.com/paper/2408.17404/) (2024)
- [GAI: Generative Agents for Innovation](https://mlsanity.com/paper/2412.18899/) (2024)
- [Risks of AI-driven product development and strategies for their mitigation](https://mlsanity.com/paper/2506.00047/) (2025)
- [RECALL-MM: A Multimodal Dataset of Consumer Product Recalls for Risk Analysis using Computational Methods and Large Language Models](https://mlsanity.com/paper/2503.23213/) (2025)
- [4D-ARE: Bridging the Attribution Gap in LLM Agent Requirements Engineering](https://mlsanity.com/paper/2601.04556/) (2026)
- [On the Prospects of Dynamic LLM Conversations in Software Development](https://mlsanity.com/paper/2608.30756/) (2026)

## Abstract (arXiv)

Requirements elicitation, a critical, yet time-consuming and challenging step in product development, often fails to capture the full spectrum of user needs. This may lead to products that fall short of expectations. This paper introduces a novel framework that leverages Large Language Models (LLMs) to automate and enhance the requirements elicitation process. LLMs are used to generate a vast array of simulated users (LLM agents), enabling the exploration of a much broader range of user needs and unforeseen use cases. These agents engage in product experience scenarios, through explaining their actions, observations, and challenges. Subsequent agent interviews and analysis uncover valuable user needs, including latent ones. We validate our framework with three experiments. First, we explore different methodologies for diverse agent generation, discussing their advantages and shortcomings. We measure the diversity of identified user needs and demonstrate that context-aware agent generation leads to greater diversity. Second, we show how our framework effectively mimics empathic lead user interviews, identifying a greater number of latent needs than conventional human interviews. Third, we showcase that LLMs can be used to analyze interviews, capture needs, and classify them as latent or not. Our work highlights the potential of using LLM agents to accelerate early-stage product development, reduce costs, and increase innovation.