Enhancing LLM-Based Human-Robot Interaction with Nuances for Diversity Awareness
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916299885510656 |
|---|---|
| author | Grassi, Lucrezia Recchiuto, Carmine Tommaso Sgorbissa, Antonio |
| author_facet | Grassi, Lucrezia Recchiuto, Carmine Tommaso Sgorbissa, Antonio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_17531 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing LLM-Based Human-Robot Interaction with Nuances for Diversity Awareness Grassi, Lucrezia Recchiuto, Carmine Tommaso Sgorbissa, Antonio Robotics Artificial Intelligence Human-Computer Interaction 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. |
| title | Enhancing LLM-Based Human-Robot Interaction with Nuances for Diversity Awareness |
| topic | Robotics Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2406.17531 |