📝MediumNLP FundamentalsPREMIUM

Perplexity & model evaluation

Derive perplexity from cross-entropy loss, understand bits-per-byte normalization, and navigate the modern LLM evaluation landscape including LLM-as-Judge and Arena Elo.

What you'll master

Perplexity derivation from cross-entropy as exponentiated average

Branching factor interpretation: effective vocabulary at each step

Bits-per-byte for tokenizer-independent comparison

Tokenizer problem: PPL incomparable across different tokenizers

PPL ≠ quality: fluency vs factuality vs usefulness

Sliding window computation with stride for long texts

LLM-as-Judge framework and its known biases

Arena Elo as the gold standard for open-ended evaluation

Benchmark contamination and the LiveBench response

Multi-level evaluation stack for production LLMs

Medium45 min readIncludes code examples, architecture diagrams, and expert-level follow-up questions.

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Code examplesArchitecture diagramsModel answersScoring rubricCommon pitfallsFollow-up Q&A

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