# Why did My Robot Just Change Personality? Prompting Guidelines for a Grounded Robot Persona in LLM-Based HRI

arXiv: https://arxiv.org/abs/2608.26182 · HTML: https://mlsanity.com/paper/2608.26182/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 21 Aug 2026.

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

- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Understanding Large-Language Model (LLM)-powered Human-Robot Interaction](https://mlsanity.com/paper/2401.03217/) (2024)
- [Applying General Turn-taking Models to Conversational Human-Robot Interaction](https://mlsanity.com/paper/2501.08946/) (2025)
- [Leveraging Large Language Models in Human-Robot Interaction: A Critical Analysis of Potential and Pitfalls](https://mlsanity.com/paper/2405.00693/) (2024)
- [A Robot-Led Intervention for Emotion Regulation: From Expression to Reappraisal](https://mlsanity.com/paper/2503.18243/) (2025)
- [What People Share With a Robot When Feeling Lonely and Stressed and How It Helps Over Time](https://mlsanity.com/paper/2504.02991/) (2025)
- [Communicative Robot Signals: Presenting a New Typology for Human-Robot Interaction](https://mlsanity.com/paper/2302.03971/) (2023)
- [GRACE: Generating Socially Appropriate Robot Actions Leveraging LLMs and Human Explanations](https://mlsanity.com/paper/2409.16879/) (2024)
- [Social Identity in Human-Agent Interaction: A Primer](https://mlsanity.com/paper/2508.16609/) (2025)
- [Expectations, Explanations, and Embodiment: Attempts at Robot Failure Recovery](https://mlsanity.com/paper/2504.07266/) (2025)
- [Designing Social Robots with Ethical, User-Adaptive Explainability in the Era of Foundation Models](https://mlsanity.com/paper/2603.00102/) (2026)
- [Towards A Framework for Levels of Anthropomorphic Deception in Robots and AI](https://mlsanity.com/paper/2604.15418/) (2026)

## Abstract (arXiv)

Large language models (LLMs) are increasingly used for verbal interaction in social robots, yet prompt design in human-robot interaction (HRI) remains underspecified. As a result, robots may present hallucinated capabilities, unclear behavioural boundaries, and misleading personas. This paper develops a framework for prompt design in LLM-based robots and introduces a structured prompt template comprising eight functional components through which robot behaviour can be specified, bounded, and adapted. The framework is grounded in a review of prior LLM-based HRI work and complemented by survey and discussion data from HRI experts gathered at the Robo-Identity workshop at IEEE RO-MAN 2025 (N=27). The qualitative findings highlight limited legibility of robot personality, the need for user adaptation, and strong ethical concerns about safety, deception, and governance. Based on these findings, we present prompting guidelines accompanied by proof-of-concept template as a structured design and reporting aid for HRI research. We argue that prompt design should be treated as a socio-technical problem rather than a minor implementation detail, requiring explicit capability boundaries, transparent behavioural assumptions, and context-sensitive safeguards to support reliable and interpretable HRI.