LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey
Project Overview
The document explores the transformative role of generative AI, particularly Large Language Models (LLMs), in education through Human-Agent collaboration systems (LLM-HAS). It emphasizes the necessity of integrating human feedback to enhance the performance, reliability, and safety of these systems. While generative AI offers significant advancements in fostering communication and interaction among users, it also presents challenges such as hallucinations, task complexity, and ethical concerns that hinder the pursuit of full autonomy in educational settings. Moreover, the document outlines various applications of generative AI in education, showcasing collaborative tools that facilitate structured communication and task orchestration. These tools not only enhance collaborative efforts but also provide innovative ways to support and engage learners, illustrating a shift from traditional educational methodologies to more dynamic, interactive practices. The findings underscore the potential of generative AI to revolutionize educational experiences, although addressing the associated risks remains crucial for their successful implementation.
Key Applications
Collaborative Gym
Context: Simulated and real-world tasks such as travel planning, data analysis, academic writing, and collaborative projects in higher education.
Implementation: Supports asynchronous and synchronous collaboration through decentralized and hierarchical conversation modes, enhancing interactions among humans and agents.
Outcomes: Enhances human-agent dynamics, improves collaborative capabilities among students, and increases satisfaction and user experience.
Challenges: Requires user engagement, effective feedback mechanisms, and coordination strategies, along with potential synchronization issues during simultaneous interactions.
ReHAC framework
Context: Software development environments.
Implementation: Agents determine optimal stages for human intervention in software development, improving efficiency and generalizability.
Outcomes: Improves development efficiency and offers advantages over traditional methods.
Challenges: Complexity of integrating multiple feedback types.
MindAgent framework
Context: Gaming environments.
Implementation: Enhances task performance through human-agent collaboration.
Outcomes: Improved user experience and satisfaction.
Challenges: Execution latency and maintaining reasoning capabilities.
FinArena
Context: Financial market analysis and forecasting, integrating human investors with AI agents.
Implementation: Facilitates collaboration between human investors and AI agents for improved investment strategies.
Outcomes: Increases investment performance and competitive returns.
Challenges: Complexity of stock market interactions and risk management.
MTOM
Context: Academic research environments, particularly for collaborative projects.
Implementation: Synchronous collaboration using decentralized conversation modes to enhance cooperation and task coordination.
Outcomes: Improved cooperation and coordination in group tasks.
Challenges: Potential synchronization issues during simultaneous interactions.
FineArena
Context: Educational technology tools for team-based learning.
Implementation: Synchronous collaboration through hierarchical conversation structures to facilitate structured communication among team members.
Outcomes: Facilitates structured communication and improves interaction quality.
Challenges: Complexity in managing hierarchical interactions.
Implementation Barriers
Technical barrier
LLMs exhibit hallucination, generating plausible but incorrect outputs. Additionally, integration of AI systems with existing educational platforms can be challenging.
Proposed Solutions: Integrate robust human feedback mechanisms to correct and guide outputs. Develop standardized APIs and training for educators on AI tools.
Complexity barrier
LLM-based agents struggle with complex tasks requiring nuanced reasoning.
Proposed Solutions: Employ human oversight and feedback to assist agents in task execution.
Safety and ethical barrier
Unintended harmful actions and societal bias can arise from LLM interactions.
Proposed Solutions: Implement safety protocols and ethical guidelines for human-agent collaboration.
User Adoption Barrier
Resistance from educators and students to adopt new technologies.
Proposed Solutions: Providing training programs and demonstrating the benefits of AI tools.
Project Team
Henry Peng Zou
Researcher
Wei-Chieh Huang
Researcher
Yaozu Wu
Researcher
Yankai Chen
Researcher
Chunyu Miao
Researcher
Hoang Nguyen
Researcher
Yue Zhou
Researcher
Weizhi Zhang
Researcher
Liancheng Fang
Researcher
Langzhou He
Researcher
Yangning Li
Researcher
Dongyuan Li
Researcher
Renhe Jiang
Researcher
Xue Liu
Researcher
Philip S. Yu
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu, Yankai Chen, Chunyu Miao, Hoang Nguyen, Yue Zhou, Weizhi Zhang, Liancheng Fang, Langzhou He, Yangning Li, Dongyuan Li, Renhe Jiang, Xue Liu, Philip S. Yu
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai