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Background

  • Background
    This paper identifies the substantial challenges faced when evaluating large language models (LLMs) as general-purpose agents, noting the complexity of benchmarking agent performance, the maintenance of partially-observable environments, and ensuring multi-round interactions.

  • Existing Work Explains that existing frameworks focus on final success rates, which do not offer sufficient insights during the evaluation process or a deep understanding of model capabilities. Existing benchmarks do not often meet the full criteria needed for multifaceted agent assessment.

Core Contributions

  • An AGENTBOARD benchmark To overcome these challenges, the authors present AGENTBOARD, a benchmark for multi-turn LLM agents accompanied by an open-source evaluation framework for detailed assessment beyond final success rates.
  • Challenge 1: Multi-turn interactions and partially-observable environments AGENTBOARD provides a more systemic and analytical approach to evaluate agents capable of multi-turn interactions in partially-observable environments, capturing incremental advancements and subtle nuances better than existing methods focusing mainly on final success rates.
  • Challenge 2: In-depth model functionality understanding AGENTBOARD introduces a granular progress rate metric for tracking detailed progress of the agents, with a comprehensive toolkit for multifaceted analysis through interactive visualization. This reveals the capabilities and limitations of LLM agents and brings the interpretability of their performance to the forefront.

Implementation and Deployment

The team developed AGENTBOARD to address these challenges, offering a comprehensive benchmark and open-source evaluation framework tailored for the analytical assessment of LLM agents. It includes a set of 9 diverse tasks and 1013 example environments covering from embodied AI to game agents, web, and tool agents. Each environment is carefully created or adapted from existing ones and verified by humans to ensure multi-round and partially observable characteristics in a unified manner. The benchmark also provides a unified progress rate metric that uncovers substantial advances in models, which might be overlooked due to small differences in success rates. Additionally, AGENTBOARD includes an analytical web panel as a toolkit for examining various dimensions of agent abilities through interactive visualization. Evaluation via AGENTBOARD offers a clear perspective on the current capabilities of LLM models as agents, such as GPT-4's extensive proficiency across multiple tasks.

Summary

Researchers introduced a new benchmark, AGENTBOARD, for evaluating multi-turn capable large language model agents, providing a granular progress rate and interactive analysis tools to deepen the understanding of LLM agent performance.