Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects
(2401.03428)Abstract
Intelligent agents stand out as a potential path toward artificial general intelligence (AGI). Thus, researchers have dedicated significant effort to diverse implementations for them. Benefiting from recent progress in LLMs, LLM-based agents that use universal natural language as an interface exhibit robust generalization capabilities across various applications -- from serving as autonomous general-purpose task assistants to applications in coding, social, and economic domains, LLM-based agents offer extensive exploration opportunities. This paper surveys current research to provide an in-depth overview of LLM-based intelligent agents within single-agent and multi-agent systems. It covers their definitions, research frameworks, and foundational components such as their composition, cognitive and planning methods, tool utilization, and responses to environmental feedback. We also delve into the mechanisms of deploying LLM-based agents in multi-agent systems, including multi-role collaboration, message passing, and strategies to alleviate communication issues between agents. The discussions also shed light on popular datasets and application scenarios. We conclude by envisioning prospects for LLM-based agents, considering the evolving landscape of AI and natural language processing.
Overview
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Rapid advancements in LLMs have enabled the creation of intelligent agents with wide applications across various fields, demonstrating vast knowledge and cognitive capabilities.
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LLM-based agents possess features like comprehensive language understanding, decision-making, adaptive learning, and the ability to perform complex tasks, thanks to their planning and memory capabilities.
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There is a need for more comprehensive and domain-specific benchmarks to accurately evaluate the performance and applicability of LLM-based agents in different sectors.
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Addressing the technological and ethical challenges facing LLM-based agents is crucial for realizing their potential in achieving AGI and ensuring their security and reliability.
Navigating the Future of LLM-Based Agents: Implications and Challenges
Expanding Horizons of LLM-Based Agents
The rapid advancements in LLMs have ushered in a new era of intelligent agent research, encompassing a broad spectrum of fields from natural sciences to engineering systems. LLM-based agents, leveraging the expansive knowledge and cognitive capabilities of LLMs, present a versatile platform for addressing complex, multi-faceted problems across various domains.
Core Components and Capabilities
LLM-based agents inherit a robust set of features including comprehensive language understanding, decision-making, and adaptive learning, which are complemented by their ability to engage in nuanced human-computer interaction and execute a plethora of tasks with minimal input. Fundamentally, these agents are defined by their planning and memory capabilities, which allow them to navigate and manipulate their environment effectively.
In the realm of multi-agent systems (MAS), these agents can assume multiple roles, adapting their strategies based on cooperative or competitive dynamics. This flexibility enables them to partake in intricate scenarios requiring coordination and negotiation, a testament to their evolving intelligence and autonomy.
Benchmarking and Performance Evaluation
Current benchmarks focus on evaluating foundational capabilities like task-solving, cooperation, and interaction fluency. However, the creation of more comprehensive and domain-specific benchmarks remains pivotal for accurately assessing the performance and applicability of LLM-based agents. The development of such benchmarks will be instrumental in propelling the field forward, ensuring that these agents can meet the evolving requirements of various sectors effectively.
Future Directions and Challenges
The journey towards achieving AGI through LLM-based agents is riddled with both technological and ethical challenges. The intrinsic limitations of LLMs, such as context length constraints and hallucinations, pose significant obstacles. Dynamic scaling and adaptability, especially in the face of rapidly expanding system demands, require innovative strategies to maintain efficiency and reliability. Moreover, the security and trustworthiness of these systems are paramount, necessitating rigorous mechanisms for permission allocation and system verification to prevent misuse and ensure safety.
Conclusion
The exploration and development of LLM-based agents stand at the confluence of groundbreaking discoveries and formidable challenges. As we venture further into this exciting domain, the potential for these agents to revolutionize a myriad of industries and scientific fields is immense. However, realizing this potential demands a concerted effort in addressing the challenges inherent in their design and deployment. The road ahead is both promising and demanding, inviting researchers and practitioners alike to contribute towards shaping the future of intelligent, autonomous agents.