MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains

MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains

Introduction

The paper "MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains" introduces the Massive Multitask Agent Understanding (MMAU) benchmark, a comprehensive framework for evaluating LLMs as human-like agents. This benchmark seeks to address the limitations of existing evaluation methods by offering a detailed examination of core agent capabilities across multiple domains, thereby providing a more granular understanding of model performance.

Motivation and Contributions

The motivation behind developing MMAU stems from the complexity and multifaceted nature of LLM capabilities. While existing benchmarks like MMLU and AGENTBOARD provide valuable insights, they often emphasize specific scenarios or task completions, neglecting the underlying skills driving these outcomes. MMAU aims to fill this gap by offering a capability-centric evaluation across diverse tasks.

The paper makes several key contributions:

1. Capability Decomposition: MMAU evaluates five essential capabilities—Understanding, Reasoning, Planning, Problem-solving, and Self-correction—across five domains (Tool-use, Directed Acyclic Graph (DAG) QA, Data Science and Machine Learning coding, Contest-level programming, and Mathematics).
2. Comprehensive Task Design: With a total of 20 tasks encompassing over 3,000 distinct prompts, the benchmark offers a robust framework for model evaluation.
3. Evaluation of 18 Representative Models: By testing 18 models, including both API-based commercial models and open-source models, MMAU provides deep insights into the strengths and limitations of current LLM agents.
4. Ensuring Reliability and Reproducibility: The benchmark's offline nature eliminates the need for complex environment setups, ensuring stable and reproducible evaluation results.

Capability-Centric Evaluation

One of the key innovations of MMAU is its focus on capability-centric evaluation. This approach allows for a detailed examination of specific skills required for different tasks, thereby providing more targeted insights into model performance.

• Understanding: Assessment includes tasks like complex instruction following, user intent understanding, and statistics parsing. For instance, the Comprehend+ task isolates basic comprehension skills from problem-solving by focusing on mathematically simple problems with complex descriptions.
• Reasoning and Planning: These capabilities are evaluated through tasks like planner-shift, where the solution generation is divided into two stages: planning and execution. This separation allows for a more precise measurement of reasoning and planning skills.
• Problem-solving: This capability is assessed through tasks like solver-shift, where models are given pre-defined plans and are evaluated based on their execution performance.
• Self-correction: The benchmark includes tasks that test an agent's ability to recognize and correct its own errors, simulating scenarios where a previous tool call or response contains an error.

Model Evaluation

The evaluation of 18 models on MMAU reveals significant performance variations across different capabilities and domains. For instance, GPT-4 family models consistently outperform others, especially in domains requiring complex reasoning and planning, like Mathematics and Contest-level programming. These models also exhibit balanced performance across all capabilities, underscoring their robust and versatile nature.

In contrast, many open-source models struggle with tasks requiring advanced capabilities like self-correction and planning. This highlights the need for further research and development to enhance these fundamental skills in LLM agents.

Implications and Future Work

The detailed evaluations provided by MMAU have several implications:

• Understanding Model Limitations: By decomposing capabilities, MMAU allows researchers to pinpoint specific areas where models fall short, facilitating more targeted improvements.
• Guidance for Model Development: The benchmark's comprehensive nature provides a clear roadmap for developing more robust and versatile LLM agents, emphasizing the importance of balanced capabilities.
• Benchmark Standardization: MMAU sets a new standard for performance assessment in the AI landscape, offering a reliable and reproducible framework for model evaluation.

Conclusion

In conclusion, the MMAU benchmark represents a significant advancement in the evaluation of LLM agents. By providing a detailed and comprehensive framework for assessing fundamental capabilities across multiple domains, MMAU not only enhances our understanding of current model performance but also guides future developments in AI research. Future iterations of MMAU should aim to include interactive tasks and additional capabilities like retrieving, memorizing, and sequential decision-making, further refining the benchmark and pushing the boundaries of AI evaluation.