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BPC

As explained in LLMs in Production

BPC is an entropy-based language-model evaluation metric measuring the average number of binary digits needed to represent each character.

BPC (bits per character) measures language-model entropy as the average number of binary digits required to represent each character. Lower values are better. section 4.2.1

Use BPC with these practical cautions:

  • Compare BPC carefully across models with different tokenization strategies; tokenization can make entropy-related scores incomparable. section 4.2.1
  • Use entropy-related metrics to monitor or compare the same model during training, while recognizing that information theory is treated only intuitively. section 4.2.1

How LLMs in Production covers it

How deep it goes
Defines average binary digits per character intuitively, but stops short of explaining entropy, perplexity, or information theory deeply.
Examples
Contrasts character-level and word-level tokenization to show why entropy-related scores cannot be compared directly.
What it assumes
Starts from an intuitive understanding of entropy while leaving information theory for further study.
What you can do after
Evaluate language-model training with an entropy metric and recognize tokenization-based comparison failures.
How it connects
Connects entropy with perplexity, tokenization, and information theory.
Cover of LLMs in Production
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