Topic
LLM Benchmarks: MMLU, HumanEval, SWE-bench & GPQA
8 posts on llm benchmarks: mmlu, humaneval, swe-bench & gpqa — part of glossary on the n4n AI blog.
Why benchmark scores don't always predict real-world use
Why llm benchmark scores real world performance often diverges, and how to evaluate models for production systems instead of chasing leaderboard rankings.
What is SWE-bench? Real-world coding tasks for LLMs
SWE-bench explained: real GitHub issues as LLM coding benchmarks, how evaluation works, and why it matters for agent development.
What is MMLU? The multitask benchmark explained
MMLU explained: what it measures, how the 57-task benchmark works, why engineers use it for model selection, and where it falls short.
HumanEval explained: how coding benchmarks work
A practical breakdown of the HumanEval benchmark — what it measures, how the evaluation works, and what the scores actually tell you about code generation quality.
How to read an LLM model card's benchmark table
Learn to decode LLM model card benchmark tables — what the scores mean, which metrics matter for your use case, and how to spot red flags before you commit.
GSM8K explained: grade-school math for LLMs
GSM8K benchmark explained — what it measures, how the 8.5K grade-school math problems work, why multi-step reasoning matters, and where the dataset falls short for evaluating modern LLMs.
GPQA explained: graduate-level science questions for AI
GPQA benchmark explained: what the graduate-level science benchmark tests, how it works, why it matters for reasoning evaluation, and where it fits in your LLM eval stack.
Chatbot Arena explained: how Elo ratings rank LLMs
Chatbot Arena uses pairwise Elo ratings from human preference votes to rank LLMs. Here's how the benchmark works, why it differs from static benchmarks, and what the scores actually mean.
More topics in glossary
- Structured Outputs & JSON Mode19
- AI Agents Fundamentals12
- Hallucination in LLMs11
- Sampling Parameters: Top-p, Top-k & Penalties11
- Context Window & Context Length10
- Fine-Tuning Fundamentals9
- Foundation Models: Base vs Instruct vs Chat9
- Model Families & Naming Conventions: GPT-5, Claude, Gemini 3, Llama 4, Mistral, DeepSeek, Qwen, Grok9
- Grounding & Fact-Checking in AI8
- Max Tokens, Stop Sequences & Output Truncation8
- Quantization Formats: GGUF, GPTQ, AWQ & INT4/INT88
- Chain-of-Thought Prompting7