# How Do You Read LLM Benchmarks? | llmnet.nl Benchmark

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# How Do You Read LLM Benchmarks?

When a new Large Language Model (LLM) is released, developers invariably show off impressive charts and benchmark scores. But what do these numbers mean in practice? On this page, we explain the most important evaluation metrics and warn you about the ways test results are sometimes presented more favorably than they actually are.

## Commonly Used Benchmarks Explained

To assess a model objectively, standardized datasets are used. These are the most common ones:

- MMLU (Massive Multitask Language Understanding): This is a test that measures general knowledge and problem-solving ability across 57 different academic and professional subjects, ranging from history to mathematics and law.

- HumanEval: A dataset specifically focused on code generation. It measures the extent to which the model can write correct Python code based on a natural language description.

- GSM8K (Grade School Math 8K): This benchmark tests the ability to think logically step-by-step using grade-school level math problems.

- MATH: A lot tougher than GSM8K; this dataset contains complex competition-level math problems.

## Common Pitfalls

High scores do not automatically mean a better model in practice. Always be alert to the following pitfalls when reading a technical report:

### 1. Data Contamination (Test set leakage)

A model can score extremely high on an exam simply because it has already memorized the answers. This happens when questions from datasets like MMLU or HumanEval are accidentally (or intentionally) included in the model's massive training dataset.

### 2. Cherry-picking

Companies often test their models on dozens of different benchmarks. Cherry-picking occurs when a developer only publishes the three charts where their new model scores slightly better than the competition, while omitting the results where they perform worse.

Tip: Always look at independent evaluations, such as the Chatbot Arena (LMSYS), where models compete blindly against each other based on prompts from real users, rather than relying solely on static academic tests.

## Example: Comparison Table

Below you can see what a neutral, standardized benchmark table should look like. We only show metrics under the exact same prompt conditions (for example: 0-shot vs 5-shot).

Model Name | 
Parameters | 
MMLU (5-shot) | 
HumanEval (0-shot) | 
GSM8K (8-shot, CoT) | 

Model A (Open Weights) | 
[X] Billion | 
[Score]% | 
[Score]% | 
[Score]% | 

Model B (Proprietary) | 
Unknown | 
[Score]% | 
[Score]% | 
[Score]% | 

Model C (Open Weights) | 
[Y] Billion | 
[Score]% | 
[Score]% | 
[Score]% | 

## Further Learning

Benchmarks are just one part of the ecosystem surrounding artificial intelligence. Do you want to know more about the underlying technology before diving into evaluations? Then read our comprehensive guide on the learning portal: [What is an LLM and how does it work?](https://leren.llmnet.nl/en/wat-is-een-transformer)

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