LLM benchmarks
What are LLM benchmarks?
Benchmarks are curated tasks designed to measure LLM capabilities in a consistent way. They often include:
- Multiple-choice questions
- Free-form answers to complex prompts
- Code generation tasks
- Ethical or factuality stress tests
These benchmarks are used in academic research, competitive leaderboards, and internal product evaluations.
Why it matters in AI/ML
Benchmarks help:
- Compare models from different providers (e.g., OpenAI, Anthropic, Cohere)
- Track progress in model capabilities
- Identify gaps in reasoning, safety, or factuality
They offer a shared language for researchers, builders, and buyers to evaluate model strengths and weaknesses.
Common LLM benchmarks
- MMLU (Massive multitask language understanding): Tests reasoning across 57 academic subjects
- TruthfulQA: Evaluates factual correctness and robustness to misinformation
- HellaSwag: Measures common-sense reasoning and plausibility
- Big-Bench (BIG-bench): Collaborative benchmark with 200+ tasks
- GSM8K: Math word problems for grade-school level reasoning
- ARC (AI2 reasoning challenge): Science questions from standardized tests
- CodeEval / HumanEval: For evaluating code generation tasks
- HELM (Holistic evaluation of language models): Framework for comparing models across multiple dimensions like fairness, robustness, and toxicity
Limitations
- Benchmarks may not reflect real-world use cases
- Many models overfit to public benchmarks
- Results vary based on prompt formatting and inference parameters
Related
Use benchmarks alongside task-specific evaluation to get a full picture of LLM performance.