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A benchmark evaluating language models on academic-style tasks to assess reasoning, knowledge, and general ability.
DESCRIPTION
AGIEval is a human-centric benchmark comprising exam-style problems to test language models’ reasoning, knowledge application, and general problem-solving. It targets realistic, high-stakes tasks across subjects to approximate human-level evaluations. Results guide model selection, prompting strategies, and safety gating. Scores should be interpreted with care due to dataset leakage risks and prompt sensitivity.
ALTERNATIVE DEFINITIONS
AGIEval Paper
We propose AGIEval, a human-centric benchmark designed to evaluate foundation models on tasks that are close to real-world usage such as college entrance exams, law school exams, and math competitions.
Emphasizes real-world, exam-like evaluation of foundation models.
AGIEval GitHub
AGIEval is a human-centric benchmark designed to evaluate foundation models in tasks that are close to real-world usage, especially exams.
Repository readme highlights focus on exam-style tasks and reproducibility.
RELATED TERMS
Benchmarking Evaluation Metrics Large Language Model Reasoning Prompting Human Evaluation Standardized Tests Zero-Shot Evaluation
COMPETING TERMS
RELATIONS
- Broader: Benchmarking
KNOWLEDGE AREAS
AI Data Science Testing & Validation AI Governance
TARGET AUDIENCE
AI Engineer Data Scientist MLOps Engineer Chief AI Officer AI Product Manager
EXTERNAL SOURCES
- Wikipedia - Benchmark (computing)
- AGIEval - Original paper and benchmark details
- AGIEval - Benchmark implementation and dataset
- Papers With Code - Leaderboard and links for AGIEval
TOP QUESTIONS
Why is AGIEval relevant for LLM assessment?
It tests reasoning and knowledge on exam-like tasks that mirror real human evaluations. This helps determine practical capabilities and gaps.
Category: Relevancy
How does AGIEval differ from MMLU?
AGIEval emphasizes high-stakes, exam-style items across fewer but harder tasks, while MMLU covers broader topics with multiple-choice questions.
Category: Alternatives
What are common mistakes when using AGIEval?
Ignoring prompt sensitivity, data leakage, or evaluation protocols. Always standardize prompts and consider contamination checks.
Category: Mistakes
How should I interpret AGIEval scores?
Use them comparatively across models and configurations. Avoid overgeneralizing; combine with other benchmarks and domain-specific tests.
Category: Opinion
Can AGIEval evaluate chain-of-thought or tool use?
It primarily targets reasoning without external tools, but variations can test CoT prompting. Tool-augmented setups need careful protocol design.
Category: Example
What is a hot topic around AGIEval?
Robust, contamination-resistant benchmarks and standardized evaluation harnesses to ensure comparability and reliability.
Category: Topic
How do I start using AGIEval?
Clone the repository, follow the evaluation scripts, and ensure your model interface and prompts conform to the provided templates.
Category: Requirements
What risks exist when relying solely on AGIEval?
Overfitting to benchmark idiosyncrasies, missing domain-specific skills, and overlooking safety and robustness dimensions.
Category: Risks
How often should I rerun AGIEval during model development?
Periodically during major model or prompt changes, and before releases. Track results with versioned configs for reproducibility.
Category: Management