Academic Benchmarks#
Academic benchmarks provide standardized evaluation methods for comparing model performance across different capabilities. These benchmarks are widely used in the research community and provide reliable, reproducible metrics for model assessment.
Prerequisites#
Set up or select an existing evaluation target.
For some benchmarks, you may need HuggingFace tokens or API keys for external services.
Using Academic Benchmarks#
Academic benchmarks use standardized datasets and evaluation protocols:
Standard Datasets: Most benchmarks include predefined datasets widely used in research.
Reproducible Metrics: Use established methodologies to calculate metrics.
Community Standards: You can compare results across different models and research groups.
Note
Some benchmarks (such as BFCL) also support custom datasets in the same format.
Choosing an Academic Benchmark#
Choose a benchmark based on the capability you want to test:
Benchmark |
Primary Use Case |
Key Metrics |
Example Tasks |
|---|---|---|---|
Code generation and programming |
pass@k, code correctness |
HumanEval, MBPP, MBPP+ |
|
Function/tool calling |
tool-call accuracy, latency |
Simple, parallel, multiple calls |
|
General language understanding |
accuracy, BLEU, F1, perplexity |
GSM8K, MMLU, IFEval, BBH |
|
Model safety and alignment |
safety scores, harm detection |
Nemotron Content Safety V2, WildGuard |
|
Advanced reasoning, language understanding, math, alignment |
exact match, math |
GPQA, MMLU, Math Test 500, AIME |
Options#
Learn about configuration examples and dataset formats for each of the following academic benchmark evaluations.
Run code generation benchmarks using the BigCode Evaluation Harness.
Assess tool-calling capabilities with BFCL.
Run academic benchmarks for general language understanding and reasoning.
Assess model safety on standard datasets.
Run quick academic benchmarks (GPQA, MMLU, Math Test 500, AIME) with a simple chat-based configuration.