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MMAU: Massive Multitask Agent Understanding(MMAU)
Holistic benchmark evaluating LLM agents across five domains with 20 tasks and 3K+ prompts, covering understanding, reasoning, planning, problem-solving, and self-correction.
Complexity: highEvaluation and Monitoring
🎯 30-Second Overview
Pattern: Holistic benchmark evaluating LLM agents across five domains with 20 tasks and 3K+ prompts, covering understanding, reasoning, planning, problem-solving, and self-correction
Why: Addresses limitations in existing evaluation methods with offline tasks eliminating complex environment setup while providing capability-centric analysis
Key Insight: Clear performance gap between commercial (GPT-4 family leads) and open-source models - many open-source lack tool-use capabilities
⚡ Quick Implementation
1Setup:Install MMAU benchmark framework from Apple's axlearn repository
2Configure:Select from 5 domains: Tool-use, DAG QA, Data Science, Programming, Mathematics
3Enable:Set up offline evaluation environment (no complex setup required)
4Evaluate:Run agent on 20 tasks across 3,220 distinct prompts spanning 64 subjects
5Analyze:Assess 5 capabilities: Understanding, Reasoning, Planning, Problem-solving, Self-correction
Example: mmau_eval = MMAU(agent=llm_agent, domains=["tool_use", "dag_qa", "data_science", "programming", "math"], offline=True)
📋 Do's & Don'ts
✅Evaluate across all 5 domains for comprehensive capability assessment
✅Use offline evaluation for stable and reproducible results
✅Analyze both domain-centric and capability-centric performance breakdowns
✅Compare against 18 representative models (API-based and open-source)
✅Leverage heterogeneous data sources: Kaggle, CodeContest, in-house tools
❌Expect open-source models to match API-based commercial performance
❌Skip tool-use evaluation - many open-source models lack this capability
❌Ignore capability decomposition - understanding individual strengths is key
❌Rely solely on aggregate scores without domain-specific analysis
❌Use as replacement for interactive evaluations - designed to complement them
🚦 When to Use
Use When
- • Comprehensive agent capability assessment across multiple domains
- • Offline evaluation requiring stable and reproducible results
- • Academic research on LLM agent capabilities and limitations
- • Comparative analysis between commercial and open-source models
- • Capability-centric evaluation beyond simple task completion
Avoid When
- • Interactive environment testing (use specialized benchmarks)
- • Single-domain evaluation needs (use domain-specific benchmarks)
- • Real-time agent interaction assessment
- • Simple task completion evaluation without capability analysis
- • Environments requiring complex interactive setups
📊 Key Metrics
Domain Performance
Breakdown across Tool-use, DAG QA, Data Science, Programming, Mathematics
Capability Assessment
Understanding, Reasoning, Planning, Problem-solving, Self-correction scores
Commercial vs Open-Source Gap
Clear performance gap with GPT-4 family leading commercial models
Task Completion Rate
Success rate across 20 meticulously designed tasks
Tool-Use Proficiency
Specialized evaluation of tool interaction capabilities
Subject Coverage
Performance across 64 subjects within the 5 core domains
💡 Top Use Cases
Academic Research: Apple's comprehensive framework for LLM agent capability analysis and limitations study
Model Comparison: Standardized evaluation of 18 representative models including GPT-4, Claude, and open-source alternatives
Capability Decomposition: Detailed analysis of Understanding, Reasoning, Planning, Problem-solving, and Self-correction abilities
Domain-Specific Assessment: Targeted evaluation across Tool-use, Data Science, Programming, Mathematics, and DAG QA
Offline Evaluation: Stable and reproducible assessment without complex environment setup requirements
References & Further Reading
Deepen your understanding with these curated resources
Contribute to this collection
Know a great resource? Submit a pull request to add it.
🧠
MMAU: Massive Multitask Agent Understanding(MMAU)
Holistic benchmark evaluating LLM agents across five domains with 20 tasks and 3K+ prompts, covering understanding, reasoning, planning, problem-solving, and self-correction.
Complexity: highEvaluation and Monitoring
🎯 30-Second Overview
Pattern: Holistic benchmark evaluating LLM agents across five domains with 20 tasks and 3K+ prompts, covering understanding, reasoning, planning, problem-solving, and self-correction
Why: Addresses limitations in existing evaluation methods with offline tasks eliminating complex environment setup while providing capability-centric analysis
Key Insight: Clear performance gap between commercial (GPT-4 family leads) and open-source models - many open-source lack tool-use capabilities
⚡ Quick Implementation
1Setup:Install MMAU benchmark framework from Apple's axlearn repository
2Configure:Select from 5 domains: Tool-use, DAG QA, Data Science, Programming, Mathematics
3Enable:Set up offline evaluation environment (no complex setup required)
4Evaluate:Run agent on 20 tasks across 3,220 distinct prompts spanning 64 subjects
5Analyze:Assess 5 capabilities: Understanding, Reasoning, Planning, Problem-solving, Self-correction
Example: mmau_eval = MMAU(agent=llm_agent, domains=["tool_use", "dag_qa", "data_science", "programming", "math"], offline=True)
📋 Do's & Don'ts
✅Evaluate across all 5 domains for comprehensive capability assessment
✅Use offline evaluation for stable and reproducible results
✅Analyze both domain-centric and capability-centric performance breakdowns
✅Compare against 18 representative models (API-based and open-source)
✅Leverage heterogeneous data sources: Kaggle, CodeContest, in-house tools
❌Expect open-source models to match API-based commercial performance
❌Skip tool-use evaluation - many open-source models lack this capability
❌Ignore capability decomposition - understanding individual strengths is key
❌Rely solely on aggregate scores without domain-specific analysis
❌Use as replacement for interactive evaluations - designed to complement them
🚦 When to Use
Use When
- • Comprehensive agent capability assessment across multiple domains
- • Offline evaluation requiring stable and reproducible results
- • Academic research on LLM agent capabilities and limitations
- • Comparative analysis between commercial and open-source models
- • Capability-centric evaluation beyond simple task completion
Avoid When
- • Interactive environment testing (use specialized benchmarks)
- • Single-domain evaluation needs (use domain-specific benchmarks)
- • Real-time agent interaction assessment
- • Simple task completion evaluation without capability analysis
- • Environments requiring complex interactive setups
📊 Key Metrics
Domain Performance
Breakdown across Tool-use, DAG QA, Data Science, Programming, Mathematics
Capability Assessment
Understanding, Reasoning, Planning, Problem-solving, Self-correction scores
Commercial vs Open-Source Gap
Clear performance gap with GPT-4 family leading commercial models
Task Completion Rate
Success rate across 20 meticulously designed tasks
Tool-Use Proficiency
Specialized evaluation of tool interaction capabilities
Subject Coverage
Performance across 64 subjects within the 5 core domains
💡 Top Use Cases
Academic Research: Apple's comprehensive framework for LLM agent capability analysis and limitations study
Model Comparison: Standardized evaluation of 18 representative models including GPT-4, Claude, and open-source alternatives
Capability Decomposition: Detailed analysis of Understanding, Reasoning, Planning, Problem-solving, and Self-correction abilities
Domain-Specific Assessment: Targeted evaluation across Tool-use, Data Science, Programming, Mathematics, and DAG QA
Offline Evaluation: Stable and reproducible assessment without complex environment setup requirements
References & Further Reading
Deepen your understanding with these curated resources
Contribute to this collection
Know a great resource? Submit a pull request to add it.