Leveraging Large Language Models to Enable More Effective and Accessible Socially Assistive Human-Robot Interaction

Konsep Inti

Large language models (LLMs) have the potential to significantly expand the current capabilities of socially assistive robots (SARs) by enabling more natural language dialog, multimodal user understanding, and flexible robot policies. However, incorporating LLMs also introduces new risks and ethical concerns that must be carefully addressed.

Abstrak

This paper surveys the potential of using large language models (LLMs) to address the core technical challenges in socially assistive robotics (SAR).
Natural Language Dialogue: Prior to LLMs, SARs relied on limited rule-based dialogue systems. LLM-powered SARs can now engage in more natural, flexible, and context-aware conversations, enabling better interactions with user populations like older adults and children with autism.
Multimodal User Understanding: Existing machine learning models for multimodal social understanding struggle to generalize across different contexts. Multimodal language models like CLIP and ALIGN show promise in zero-shot and few-shot adaptation, indicating their potential to enable more generalizable and accurate multimodal social understanding for SARs.
LLMs as Robot Policies: LLMs may help relax the constraints of existing approaches like rule-based systems and reinforcement learning, allowing SARs to form more flexible and human-like policies for spontaneous tasks, educational interactions, and personalized user support.
While LLMs offer great potential, the authors also discuss the risks and safety considerations, such as amplifying unfairness, data privacy concerns, and hallucination behaviors, that must be carefully addressed before deploying LLM-powered SARs with vulnerable populations.

Statistik

SARs have the potential to lower socio-economic barriers and provide personalized therapies, companionship, and education to diverse user populations.
Existing SAR interactions have not yet achieved human-level social intelligence and efficacy in areas like multimodal understanding and natural dialog.
Recent advances in LLMs have shown tremendous success in tasks like language modeling, question answering, and robot planning, indicating their potential to address the core technical challenges of SARs.

Kutipan

"LLM-powered SARs are able to produce varied dialogue while staying on topic."
"MLMs may also be capable of adapting to novel social context for more generalizable and accurate multimodal social understanding."
"Research using LLMs as robot policies has not yet explored how to enable SARs to form policies for spontaneous tasks, engage users in educational tasks while keeping them challenged and encouraged, reason about user intent and needs with partially observable information, and enable personalized policies to quickly align with each user's unique needs."

Wawasan Utama Disaring Dari

How Can Large Language Models Enable Better Socially Assistive Human-Robot Interaction

by Zhon... pada arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00938.pdf

Pertanyaan yang Lebih Dalam

How can we ensure the safety and trustworthiness of LLM-powered SARs before deploying them with vulnerable populations?

To ensure the safety and trustworthiness of Large Language Model (LLM)-powered Socially Assistive Robots (SARs) before deploying them with vulnerable populations, several key steps need to be taken:

Explainability and Transparency: It is crucial to enhance the explainability of LLMs to understand the reasoning behind their decisions. This transparency can help identify potential biases or errors in the system's output.

Ethical Data Usage: Implement strict protocols for data collection, storage, and usage to protect the privacy and security of vulnerable populations. Ensuring that personal data is used ethically and with consent is paramount.

Bias Detection and Mitigation: Regularly audit the LLMs for biases and take proactive measures to mitigate them. Bias detection algorithms can help identify and address any unfairness in the system.

Safety Protocols: Develop robust safety protocols to prevent any harmful behaviors or actions by the SARs. Implement fail-safes and emergency shutdown procedures to ensure user safety.

User Testing and Feedback: Conduct extensive user testing with diverse groups, including representatives from vulnerable populations, to gather feedback on the SAR's performance and safety. Incorporate this feedback into system improvements.

Regulatory Compliance: Ensure that the deployment of LLM-powered SARs complies with relevant regulations and standards for assistive technologies. Regular audits and compliance checks can help maintain safety standards.

By following these steps and continuously monitoring the system's performance, we can enhance the safety and trustworthiness of LLM-powered SARs before deploying them with vulnerable populations.

What are the potential negative societal impacts of biases and hallucinations in LLM-powered SARs, and how can we mitigate these risks?

Biases and hallucinations in Large Language Model (LLM)-powered Socially Assistive Robots (SARs) can have significant negative societal impacts, including:

Reinforcement of Stereotypes: Biases in LLMs can perpetuate existing societal biases and stereotypes, leading to discriminatory behavior towards vulnerable populations.

Misinformation and Miscommunication: Hallucinations in LLMs can result in the dissemination of false information or inappropriate responses, causing confusion and potential harm to users.

Privacy Violations: Biased or hallucinating LLMs may compromise user privacy by mishandling sensitive information or sharing it inappropriately.

To mitigate these risks, the following strategies can be implemented:

Bias Detection and Correction: Regularly audit LLMs for biases and implement mechanisms to correct these biases. Bias mitigation techniques such as debiasing algorithms can help reduce the impact of biases on the system.

Explainability and Transparency: Enhance the explainability of LLMs to understand how decisions are made. Transparent systems can help identify and address hallucinations or false outputs.

User Education: Educate users about the limitations of LLM-powered SARs and how to interpret their responses. Providing users with information on how the system works can help mitigate potential negative impacts.

Ethical Guidelines: Develop and adhere to strict ethical guidelines for the use of LLMs in SARs. Ethical frameworks can guide the development and deployment of these systems in a responsible manner.

By implementing these strategies and continuously monitoring the system for biases and hallucinations, we can mitigate the negative societal impacts of LLM-powered SARs.

How can we leverage the vast knowledge and reasoning capabilities of LLMs to enable SARs to provide personalized, empathetic, and transformative support for individuals with diverse needs and backgrounds?

To leverage the vast knowledge and reasoning capabilities of Large Language Models (LLMs) for Socially Assistive Robots (SARs) to provide personalized, empathetic, and transformative support for individuals with diverse needs and backgrounds, the following approaches can be adopted:

Personalization: Utilize the knowledge base of LLMs to personalize interactions with users based on their preferences, needs, and backgrounds. Tailoring responses and actions to individual users can enhance the effectiveness of SAR interventions.

Empathetic Communication: Train LLMs to understand and respond empathetically to users' emotional states and needs. Incorporating emotional intelligence into the system can create a more supportive and empathetic interaction.

Transformative Interventions: Use the reasoning capabilities of LLMs to identify patterns, trends, and insights in user data to offer transformative interventions. Predictive analytics and data-driven decision-making can enhance the impact of SAR support.

Multimodal Integration: Integrate multimodal data (language, visual, and audio) to provide a holistic understanding of users' cognitive-affective states. Combining different modalities can enable SARs to offer more comprehensive and tailored support.

Continuous Learning: Implement mechanisms for SARs to continuously learn and adapt based on user feedback and outcomes. Adaptive learning algorithms can improve the system's effectiveness over time.

By leveraging these strategies and harnessing the capabilities of LLMs, SARs can offer personalized, empathetic, and transformative support to individuals with diverse needs and backgrounds, enhancing the quality and impact of social interactions.