# DiverseDialogue: A Methodology for Designing Chatbots with Human-Like Diversity

arXiv: https://arxiv.org/abs/2409.00262 · HTML: https://mlsanity.com/paper/2409.00262/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 26 references that are in the map.
- First posted on arXiv 30 Aug 2024.

## Builds on (most cited first, 12 of 12)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies](https://mlsanity.com/paper/2208.10264/) (2022)
- [Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents](https://mlsanity.com/paper/2405.02957/) (2024)
- [PersonaGym: Evaluating Persona Agents and LLMs](https://mlsanity.com/paper/2407.18416/) (2024)
- [Can Large Language Models Serve as Rational Players in Game Theory? A Systematic Analysis](https://mlsanity.com/paper/2312.05488/) (2023)
- [From Distributional to Overton Pluralism: Investigating Large Language Model Alignment](https://mlsanity.com/paper/2406.17692/) (2024)
- [Spot The Bot: A Robust and Efficient Framework for the Evaluation of Conversational Dialogue Systems](https://mlsanity.com/paper/2010.02140/) (2020)
- [CycleAlign: Iterative Distillation from Black-box LLM to White-box Models for Better Human Alignment](https://mlsanity.com/paper/2310.16271/) (2023)
- [Advancing an Interdisciplinary Science of Conversation: Insights from a Large Multimodal Corpus of Human Speech](https://mlsanity.com/paper/2203.00674/) (2022)
- [Vernacular? I Barely Know Her: Challenges with Style Control and Stereotyping](https://mlsanity.com/paper/2406.12679/) (2024)

## Built on by (most cited first, 4 of 4)

- [Towards a Design Guideline for RPA Evaluation: A Survey of Large Language Model-Based Role-Playing Agents](https://mlsanity.com/paper/2502.13012/) (2025)
- [LLM-Based Social Simulations Require a Boundary](https://mlsanity.com/paper/2506.19806/) (2025)
- [MEDAL: A Framework for Benchmarking LLMs as Multilingual Open-Domain Dialogue Evaluators](https://mlsanity.com/paper/2505.22777/) (2025)
- [Ask or Answer: A Decision Framework for Multi-Turn Health Misinformation Intervention](https://mlsanity.com/paper/2608.21721/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs), which simulate human users, are frequently employed to evaluate chatbots in applications such as tutoring and customer service. Effective evaluation necessitates a high degree of human-like diversity within these simulations. In this paper, we demonstrate that conversations generated by GPT-4o mini, when used as simulated human participants, systematically differ from those between actual humans across multiple linguistic features. These features include topic variation, lexical attributes, and both the average behavior and diversity (variance) of the language used. To address these discrepancies, we propose an approach that automatically generates prompts for user simulations by incorporating features derived from real human interactions, such as age, gender, emotional tone, and the topics discussed. We assess our approach using differential language analysis combined with deep linguistic inquiry. Our method of prompt optimization, tailored to target specific linguistic features, shows significant improvements. Specifically, it enhances the human-likeness of LLM chatbot conversations, increasing their linguistic diversity. On average, we observe a 54 percent reduction in the error of average features between human and LLM-generated conversations. This method of constructing chatbot sets with human-like diversity holds great potential for enhancing the evaluation process of user-facing bots.