AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents (2401.13178v1)

Abstract: Evaluating LLMs as general-purpose agents is essential for understanding their capabilities and facilitating their integration into practical applications. However, the evaluation process presents substantial challenges. A primary obstacle is the benchmarking of agent performance across diverse scenarios within a unified framework, especially in maintaining partially-observable environments and ensuring multi-round interactions. Moreover, current evaluation frameworks mostly focus on the final success rate, revealing few insights during the process and failing to provide a deep understanding of the model abilities. To address these challenges, we introduce AgentBoard, a pioneering comprehensive benchmark and accompanied open-source evaluation framework tailored to analytical evaluation of LLM agents. AgentBoard offers a fine-grained progress rate metric that captures incremental advancements as well as a comprehensive evaluation toolkit that features easy assessment of agents for multi-faceted analysis through interactive visualization. This not only sheds light on the capabilities and limitations of LLM agents but also propels the interpretability of their performance to the forefront. Ultimately, AgentBoard serves as a significant step towards demystifying agent behaviors and accelerating the development of stronger LLM agents.

Analytical Evaluation of Multi-Turn LLM Agents Using AGENTBOARD

The paper presents "AGENTBOARD," an advanced benchmarking and evaluation framework designed to assess LLM agents capable of performing multi-round interactions across diverse environments. This initiative addresses fundamental challenges in current LLM evaluation methods, offering a nuanced analysis beyond simplistic success rate metrics.

LLMs, owing to their potential as general-purpose agents, must be gauged on comprehensive competencies that include understanding dynamic environments and engaging in sustained dialogue or task-solving sequences. Existing benchmarks fail to encompass these dimensions, focusing predominantly on final outcomes with limited insight into agent behavior throughout interaction processes. AGENTBOARD aims to fill this gap by providing both a benchmarking suite and an analytical toolbox which facilitate in-depth evaluation, fostering better interpretability of LLM capabilities.

Framework and Methodology

AGENTBOARD organizes tasks across four major categories: embodied AI, web-based environments, games, and tool-using scenarios. Each environment demands unique skill sets such as spatial navigation, strategic planning, and self-reflection. The authors argue that these complex tasks mirror real-world applications more closely and offer a robust platform to test LLMs as genuine interactive agents.

A significant innovation in AGENTBOARD is its emphasis on progress rate tracking, measured through defined subgoals within task sequences rather than simply at endpoints. This allows for a richer analysis of how agents improve over time, highlighting partial completions that conventional success metrics might overlook. For instance, tracking this progress in a partially observable environment helps ascertain an agent's exploratory and adaptive strategies.

The paper evaluates several models, including proprietary ones like GPT-4 and various open-weight alternatives. The results affirm the superiority of proprietary models, particularly in handling intricate, context-dependent tasks. GPT-4, for example, exhibits a remarkable balance across multiple dimensions such as memory retention and world modeling, substantiating its leading position in current LLM capabilities.

Key Findings and Implications

The analysis reaffirms that grounding accuracy—a term denoting the agent's ability to generate executable actions—is a significant factor influencing performance overall. Proprietary models outperform open-weight ones, indicating a gap in capabilities that could be either attributed to model size, richer training data, or more refined architectures.

Furthermore, AGENTBOARD exposes interesting trends in agent behavior, such as the plateauing of progress rates in tasks requiring long-term planning and strategy execution. This highlights constraints in existing models' ability to maintain performance over extended interactions, directing future research towards enhancing context management and decision-making in prolonged scenarios.

Future Directions

The paper lays the groundwork for more sophisticated evaluations of LLM agents, pinpointing areas for further research. A key recommendation is to enhance the analytical dimensions of agent evaluation by integrating more detailed sub-skills analysis, extending beyond current capabilities to include more nuanced aspects such as real-time learning and adaptation.

Future developments could involve enriching the environments in AGENTBOARD to cover more practical and industry-relevant applications, aiding in the translation of theoretical benchmarks to applied AI systems. The open-source nature of the evaluation framework encourages widespread academic engagement, potentially accelerating advancements in constructing robust, capable LLM agents.

In sum, AGENTBOARD represents a significant step in evolving LLM evaluations, promising to deepen our understanding of interactive LLMs and their real-world applications. The framework's approach sets a high standard for future benchmarks aiming to unravel the complexities of agentic AI behaviors.