# Vibe Check: Understanding the Effects of LLM-Based Conversational Agents' Personality and Alignment on User Perceptions in Goal-Oriented Tasks

arXiv: https://arxiv.org/abs/2509.09870 · HTML: https://mlsanity.com/paper/2509.09870/

## Key facts

- Cited by 4 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 28 of its 155 references that are in the map.
- First posted on arXiv 11 Sep 2025.

## 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, 28 of 28)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2204.05862/) (2022)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [Toxicity in ChatGPT: Analyzing Persona-assigned Language Models](https://mlsanity.com/paper/2304.05335/) (2023)
- [PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits](https://mlsanity.com/paper/2305.02547/) (2023)
- [Quantifying the Persona Effect in LLM Simulations](https://mlsanity.com/paper/2402.10811/) (2024)
- [Evaluating and Inducing Personality in Pre-trained Language Models](https://mlsanity.com/paper/2206.07550/) (2022)
- [Context-faithful Prompting for Large Language Models](https://mlsanity.com/paper/2303.11315/) (2023)
- [Evaluating Large Language Model Biases in Persona-Steered Generation](https://mlsanity.com/paper/2405.20253/) (2024)
- [From Hard Refusals to Safe-Completions: Toward Output-Centric Safety Training](https://mlsanity.com/paper/2508.09224/) (2025)
- [Virtual Personas for Language Models via an Anthology of Backstories](https://mlsanity.com/paper/2407.06576/) (2024)
- [Are Personalized Stochastic Parrots More Dangerous? Evaluating Persona Biases in Dialogue Systems](https://mlsanity.com/paper/2310.05280/) (2023)
- [The Personality Illusion: Revealing Dissociation Between Self-Reports & Behavior in LLMs](https://mlsanity.com/paper/2509.03730/) (2025)
- [Be Friendly, Not Friends: How LLM Sycophancy Shapes User Trust](https://mlsanity.com/paper/2502.10844/) (2025)
- [Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Review](https://mlsanity.com/paper/2501.12557/) (2025)
- [Controlling Personality Style in Dialogue with Zero-Shot Prompt-Based Learning](https://mlsanity.com/paper/2302.03848/) (2023)
- [Towards Designing a ChatGPT Conversational Companion for Elderly People](https://mlsanity.com/paper/2304.09866/) (2023)
- [Towards Understanding Emotional Intelligence for Behavior Change Chatbots](https://mlsanity.com/paper/1907.10664/) (2019)
- [Impacts of Personal Characteristics on User Trust in Conversational Recommender Systems](https://mlsanity.com/paper/2203.12981/) (2022)
- [Like My Aunt Dorothy: Effects of Conversational Styles on Perceptions, Acceptance and Metaphorical Descriptions of Voice Assistants during Later Adulthood](https://mlsanity.com/paper/2309.11681/) (2023)
- [Human-like informative conversations: Better acknowledgements using conditional mutual information](https://mlsanity.com/paper/2104.07831/) (2021)
- [Examining Humanness as a Metaphor to Design Voice User Interfaces](https://mlsanity.com/paper/2405.07458/) (2024)
- [A Survey of Personality, Persona, and Profile in Conversational Agents and Chatbots](https://mlsanity.com/paper/2401.00609/) (2023)
- [Designing AI Personalities: Enhancing Human-Agent Interaction Through Thoughtful Persona Design](https://mlsanity.com/paper/2410.22744/) (2024)
- [Toward Metaphor-Fluid Conversation Design for Voice User Interfaces](https://mlsanity.com/paper/2502.11554/) (2025)
- [Expose Uncertainty, Instill Distrust, Avoid Explanations: Towards Ethical Guidelines for AI](https://mlsanity.com/paper/2112.01281/) (2021)
- [Personas Evolved: Designing Ethical LLM-Based Conversational Agent Personalities](https://mlsanity.com/paper/2502.20513/) (2025)

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

- [Same Voice, Different Lab: On the Homogenization of Frontier LLM Personalities](https://mlsanity.com/paper/2605.02897/) (2026)
- [Misalignment Has a Personality: A Big Five Account of Emergent Misalignment](https://mlsanity.com/paper/2607.26389/) (2026)
- [Annie, Are You Okay? How Style- and Context-Based Personalization Shape AI-Assisted Decision-Making](https://mlsanity.com/paper/2609.24644/) (2026)
- [Deflecting the Value Compass: Interacting with Large Language Models Temporarily Shifts Human Value Priorities Toward Personal Focus](https://mlsanity.com/paper/2609.25586/) (2026)

## Abstract (arXiv)

Large language models (LLMs) enable conversational agents (CAs) to express distinctive personalities, raising new questions about how such designs shape user perceptions. This study investigates how personality expression levels and user-agent personality alignment influence perceptions in goal-oriented tasks. In a between-subjects experiment (N=150), participants completed travel planning with CAs exhibiting low, medium, or high expression across the Big Five traits, controlled via our novel Trait Modulation Keys framework. Results revealed an inverted-U relationship: medium expression produced the most positive evaluations across Intelligence, Enjoyment, Anthropomorphism, Intention to Adopt, Trust, and Likeability, significantly outperforming both extremes. Personality alignment further enhanced outcomes, with Extraversion and Emotional Stability emerging as the most influential traits. Cluster analysis identified three distinct compatibility profiles, with "Well-Aligned" users reporting substantially positive perceptions. These findings demonstrate that personality expression and strategic trait alignment constitute optimal design targets for CA personality, offering design implications as LLM-based CAs become increasingly prevalent.