# On the Effectiveness of Creating Conversational Agent Personalities Through Prompting

arXiv: https://arxiv.org/abs/2310.11182 · HTML: https://mlsanity.com/paper/2310.11182/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 8 of its 52 references that are in the map.
- First posted on arXiv 17 Oct 2023.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 8 of 8)

- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good](https://mlsanity.com/paper/1906.06725/) (2019)
- [Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4](https://mlsanity.com/paper/2304.03439/) (2023)
- [The Programmer's Assistant: Conversational Interaction with a Large Language Model for Software Development](https://mlsanity.com/paper/2302.07080/) (2023)
- [If I Hear You Correctly: Building and Evaluating Interview Chatbots with Active Listening Skills](https://mlsanity.com/paper/2002.01862/) (2020)
- [Maximizing Stylistic Control and Semantic Accuracy in NLG: Personality Variation and Discourse Contrast](https://mlsanity.com/paper/1907.09527/) (2019)
- [Is ChatGPT a Biomedical Expert? -- Exploring the Zero-Shot Performance of Current GPT Models in Biomedical Tasks](https://mlsanity.com/paper/2306.16108/) (2023)

## Built on by (most cited first, 10 of 10)

- [LLM Agents in Interaction: Measuring Personality Consistency and Linguistic Alignment in Interacting Populations of Large Language Models](https://mlsanity.com/paper/2402.02896/) (2024)
- [The Effects of Embodiment and Personality Expression on Learning in LLM-based Educational Agents](https://mlsanity.com/paper/2407.10993/) (2024)
- [Self-assessment, Exhibition, and Recognition: a Review of Personality in Large Language Models](https://mlsanity.com/paper/2406.17624/) (2024)
- [An Empirical Categorization of Prompting Techniques for Large Language Models: A Practitioner's Guide](https://mlsanity.com/paper/2402.14837/) (2024)
- [Curiosity by Design: An LLM-based Coding Assistant Asking Clarification Questions](https://mlsanity.com/paper/2507.21285/) (2025)
- [Giving AI Personalities Leads to More Human-Like Reasoning](https://mlsanity.com/paper/2502.14155/) (2025)
- [Promoting Online Safety by Simulating Unsafe Conversations with LLMs](https://mlsanity.com/paper/2507.22267/) (2025)
- [PILOT: Steering Synthetic Data Generation with Psychological & Linguistic Output Targeting](https://mlsanity.com/paper/2509.15447/) (2025)
- [Student Development Agent: Risk-free Simulation for Evaluating AIED Innovations](https://mlsanity.com/paper/2510.09183/) (2025)
- [Chatbots to strengthen democracy: An interdisciplinary seminar to train identifying argumentation techniques of science denial](https://mlsanity.com/paper/2511.17678/) (2025)

## Abstract (arXiv)

In this work, we report on the effectiveness of our efforts to tailor the personality and conversational style of a conversational agent based on GPT-3.5 and GPT-4 through prompts. We use three personality dimensions with two levels each to create eight conversational agents archetypes. Ten conversations were collected per chatbot, of ten exchanges each, generating 1600 exchanges across GPT-3.5 and GPT-4. Using Linguistic Inquiry and Word Count (LIWC) analysis, we compared the eight agents on language elements including clout, authenticity, and emotion. Four language cues were significantly distinguishing in GPT-3.5, while twelve were distinguishing in GPT-4. With thirteen out of a total nineteen cues in LIWC appearing as significantly distinguishing, our results suggest possible novel prompting approaches may be needed to better suit the creation and evaluation of persistent conversational agent personalities or language styles.