# Personalised Explanations in Long-term Human-Robot Interactions

arXiv: https://arxiv.org/abs/2507.03049 · HTML: https://mlsanity.com/paper/2507.03049/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 37 references that are in the map.
- First posted on arXiv 3 Jul 2025.

## Builds on (most cited first, 15 of 15)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [TidyBot: Personalized Robot Assistance with Large Language Models](https://mlsanity.com/paper/2305.05658/) (2023)
- [REFLECT: Summarizing Robot Experiences for Failure Explanation and Correction](https://mlsanity.com/paper/2306.15724/) (2023)
- [Optimization Methods for Personalizing Large Language Models through Retrieval Augmentation](https://mlsanity.com/paper/2404.05970/) (2024)
- [Personalized Pieces: Efficient Personalized Large Language Models through Collaborative Efforts](https://mlsanity.com/paper/2406.10471/) (2024)
- [Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models](https://mlsanity.com/paper/2310.20081/) (2023)
- [Personalized Large Language Models](https://mlsanity.com/paper/2402.09269/) (2024)
- [LLMs + Persona-Plug = Personalized LLMs](https://mlsanity.com/paper/2409.11901/) (2024)
- [Doing Personal LAPS: LLM-Augmented Dialogue Construction for Personalized Multi-Session Conversational Search](https://mlsanity.com/paper/2405.03480/) (2024)
- [I Can Tell What I am Doing: Toward Real-World Natural Language Grounding of Robot Experiences](https://mlsanity.com/paper/2411.12960/) (2024)
- [Not all users are the same: Providing personalized explanations for sequential decision making problems](https://mlsanity.com/paper/2106.12207/) (2021)
- [Explaining Autonomy: Enhancing Human-Robot Interaction through Explanation Generation with Large Language Models](https://mlsanity.com/paper/2402.04206/) (2024)
- [Towards Balancing Preference and Performance through Adaptive Personalized Explainability](https://mlsanity.com/paper/2504.13856/) (2025)
- [Towards Personalized Explanation of Robot Path Planning via User Feedback](https://mlsanity.com/paper/2011.00524/) (2020)

## Abstract (arXiv)

In the field of Human-Robot Interaction (HRI), a fundamental challenge is to facilitate human understanding of robots. The emerging domain of eXplainable HRI (XHRI) investigates methods to generate explanations and evaluate their impact on human-robot interactions. Previous works have highlighted the need to personalise the level of detail of these explanations to enhance usability and comprehension. Our paper presents a framework designed to update and retrieve user knowledge-memory models, allowing for adapting the explanations' level of detail while referencing previously acquired concepts. Three architectures based on our proposed framework that use Large Language Models (LLMs) are evaluated in two distinct scenarios: a hospital patrolling robot and a kitchen assistant robot. Experimental results demonstrate that a two-stage architecture, which first generates an explanation and then personalises it, is the framework architecture that effectively reduces the level of detail only when there is related user knowledge.