# A Social Robot with Inner Speech for Dietary Guidance

arXiv: https://arxiv.org/abs/2505.08664 · HTML: https://mlsanity.com/paper/2505.08664/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 32 references that are in the map.
- First posted on arXiv 13 May 2025.

## Builds on (most cited first, 9 of 9)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Improved Trust in Human-Robot Collaboration with ChatGPT](https://mlsanity.com/paper/2304.12529/) (2023)
- [VoicePilot: Harnessing LLMs as Speech Interfaces for Physically Assistive Robots](https://mlsanity.com/paper/2404.04066/) (2024)
- [Enhancing Multilingual Information Retrieval in Mixed Human Resources Environments: A RAG Model Implementation for Multicultural Enterprise](https://mlsanity.com/paper/2401.01511/) (2024)
- [On-Device LLMs for Home Assistant: Dual Role in Intent Detection and Response Generation](https://mlsanity.com/paper/2502.12923/) (2025)
- [Human Latency Conversational Turns for Spoken Avatar Systems](https://mlsanity.com/paper/2404.16053/) (2024)
- [Leveraging Large Language Models in Human-Robot Interaction: A Critical Analysis of Potential and Pitfalls](https://mlsanity.com/paper/2405.00693/) (2024)
- [Gaze-Based Intention Recognition for Human-Robot Collaboration](https://mlsanity.com/paper/2405.07570/) (2024)

## Abstract (arXiv)

We explore the use of inner speech as a mechanism to enhance transparency and trust in social robots for dietary advice. In humans, inner speech structures thought processes and decision-making; in robotics, it improves explainability by making reasoning explicit. This is crucial in healthcare scenarios, where trust in robotic assistants depends on both accurate recommendations and human-like dialogue, which make interactions more natural and engaging. Building on this, we developed a social robot that provides dietary advice, and we provided the architecture with inner speech capabilities to validate user input, refine reasoning, and generate clear justifications. The system integrates large language models for natural language understanding and a knowledge graph for structured dietary information. By making decisions more transparent, our approach strengthens trust and improves human-robot interaction in healthcare. We validated this by measuring the computational efficiency of our architecture and conducting a small user study, which assessed the reliability of inner speech in explaining the robot's behavior.