# Enhancing LLM-Based Human-Robot Interaction with Nuances for Diversity Awareness

arXiv: https://arxiv.org/abs/2406.17531 · HTML: https://mlsanity.com/paper/2406.17531/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 25 Jun 2024.

## Builds on (most cited first, 7 of 7)

- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [ChatGPT for Robotics: Design Principles and Model Abilities](https://mlsanity.com/paper/2306.17582/) (2023)
- [Diversity-aware social robots meet people: beyond context-aware embodied AI](https://mlsanity.com/paper/2207.05372/) (2022)
- [Sustainable Cloud Services for Verbal Interaction with Embodied Agents](https://mlsanity.com/paper/2203.02606/) (2022)

## Built on by (most cited first, 3 of 3)

- [Heuristic-Induced Multimodal Risk Distribution Jailbreak Attack for Multimodal Large Language Models](https://mlsanity.com/paper/2412.05934/) (2024)
- [Can LLMs Grasp Implicit Cultural Values? Benchmarking LLMs' Cultural Intelligence with CQ-Bench](https://mlsanity.com/paper/2504.01127/) (2025)
- [Encoding Inequity: Examining Demographic Bias in LLM-Driven Robot Caregiving](https://mlsanity.com/paper/2503.05765/) (2025)

## Abstract (arXiv)

This paper presents a system for diversity-aware autonomous conversation leveraging the capabilities of large language models (LLMs). The system adapts to diverse populations and individuals, considering factors like background, personality, age, gender, and culture. The conversation flow is guided by the structure of the system's pre-established knowledge base, while LLMs are tasked with various functions, including generating diversity-aware sentences. Achieving diversity-awareness involves providing carefully crafted prompts to the models, incorporating comprehensive information about users, conversation history, contextual details, and specific guidelines. To assess the system's performance, we conducted both controlled and real-world experiments, measuring a wide range of performance indicators.