LLM Benchmarks Guide
Evaluating Large Language Models is notoriously difficult. Unlike traditional software, AI output is probabilistic and subjective. Benchmarks provide standardized sets of questions or tasks to measure a model’s capabilities across different dimensions.
Core Reasoning Benchmarks
These benchmarks measure how well a model can “think” through complex problems.
1. MMLU (Massive Multitask Language Understanding)
MMLU is the “Gold Standard” for general knowledge. It cover 57 subjects across STEM, the humanities, social sciences, and more.
- Measure: World knowledge and problem-solving.
- Elite Score: >85% (GPT-4 / Claude 3 Opus level).
2. GSM8K (Grade School Math 8K)
High-quality grade school math word problems.
- Measure: Multi-step mathematical reasoning.
- Why it matters: It’s hard to solve these by simple pattern matching; the model must “think” sequentially.
3. HumanEval & MBPP
HumanEval and MBPP measure coding ability.
- Measure: Python code generation.
- HumanEval: Measures zero-shot code generation from 164 hand-written tasks.
Evaluation Dimensions
| Benchmark | Dimension | Difficulty |
|---|---|---|
| MMLU | General Knowledge | High (University level) |
| GSM8K | Math Reasoning | Medium (Grade school) |
| HumanEval | Programming | High (Logic testing) |
| TriviaQA | Fact Retrieval | Variable |
| HellaSwag | Common Sense | Low (Basic logic) |
The Data Contamination Problem
A major issue in modern AI is contamination. Since most benchmark questions are public, they often end up in the model’s massive training data.
- Symptoms: A model gets a high score on a benchmark but fails at simple variations of the same questions.
- Solution: Researchers use “Private Benchmarks” or “Live Evals” like LMSYS Chatbot Arena.
Chatbot Arena (Human Preference)
The LMSYS Chatbot Arena uses a double-blind human voting system. Humans are given the same prompt for two anonymous models and vote on which answer is better.
- ELO Rating: Models are ranked using a chess-style ELO system.
- Significance: Most researchers consider this the most “honest” measure of how a model feels to use.
Related Concepts
- MMLU Benchmark - Detailed deep dive.
- HumanEval - Measuring code quality.
- Fine-Tuning - Optimization through training.
- Chain of Thought (CoT) - Technique to improve benchmark scores.