Evaluating LLMs as versatile agents is crucial for their integration into practical applications. However, existing evaluation frameworks face challenges in comparing diverse scenarios, maintaining partially observable environments, and capturing multi-round interactions. Current evaluations often focus on a simplified metric of the final success rate, which provides limited information about complex processes. The complexity of agent tasks, which involve multi-round interactions and decision making based on extensive context, requires a more detailed and systematic evaluation approach. Addressing the need for task diversity and comprehensive assessments in challenging environments is essential to advancing the field.
Researchers from the University of Hong Kong, Zhejiang University, Shanghai Jiao Tong University, Tsinghua University, College of Engineering, Westlake University, and Hong Kong University of Science and technology have developed AgentBoard. AgentBoard is an innovative open source and benchmark assessment framework for analyzing LLM agents. AgentBoard features a detailed progress rate metric and a comprehensive toolset for interactive visualization, shedding light on the capabilities and limitations of LLM agents. With nine diverse tasks and 1013 environments, AgentBoard covers embedded ai, game agents, web agents, and tool agents, ensuring multi-round and partially observable features.
The study delves into the multifaceted capabilities of LLMs as decision-making agents. While reinforcement learning provides general solutions, LLMs excel at decision-making with emergent reasoning and instruction-following skills, demonstrating impressive zero-shot generalization. Techniques such as contextual prompts allow LLMs to generate executable actions and are repurposed by specialized training methods to become expert agents. The research compares general and agent-specific LLMs, addressing dimensions such as basic goals, world modeling, step-by-step planning, and self-reflection.
AgentBoard is a comprehensive assessment and referral framework that focuses on LLMs as versatile agents. It employs a detailed progress rate metric and a comprehensive evaluation toolset for nuanced analysis of LLM agents in text-based environments. The method involves maintaining partially observable environments and ensuring multi-round interactions. AgentBoard facilitates easy assessment through interactive visualization, providing insight into the capabilities and limitations of LLM agents. The benchmark, which features manually defined subgoals, introduces a unified progress rate metric that highlights substantial model advancements beyond traditional success rates. The accessible and customizable AgentBoard evaluation framework enables detailed analysis of agent capabilities, emphasizing the importance of analytical evaluation for LLMs, including GPT-4 and promising open source LLMs such as DeepSeek LLM and Lemur.
AgentBoard is a framework for evaluating LLMs as general-purpose agents. It offers a progress rate metric that captures incremental progress and a set of tools for multifaceted analysis. Proprietary LLMs outperform open models, and GPT-4 shows better performance. Code LLMs demonstrate relatively superior performance among open weight models. Open weight models show weak performance in the Games category, indicating the need to improve planning capabilities. Success rates in the Tools category are low, but open models offer comparatively higher progress rates.
In conclusion, AgentBoard is a tool for evaluating LLMs as general-purpose agents. Provides a comprehensive set of assessment tools and an interactive visualization web dashboard. Proprietary LLMs perform better than open models, and GPT-4 performs best in the Games and Embedded ai categories. Code LLMs such as DeepSeek-67b and CodeLlama-34b demonstrate relatively good performance among open models, highlighting the importance of having strong coding skills. Open weight models show weak performance in the Games category, indicating the need to improve planning capabilities. Open models show effectiveness in the use of tools, but need to improve the summary of the information returned by these tools in the Tools category.
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Sana Hassan, a consulting intern at Marktechpost and a dual degree student at IIT Madras, is passionate about applying technology and artificial intelligence to address real-world challenges. With a strong interest in solving practical problems, she brings a new perspective to the intersection of ai and real-life solutions.
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