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.

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