How do we use perplexity to choose between models?

How It Works:

Evaluate candidate models on a held-out dataset; select the one with the best trade-off between low perplexity and inference speed/cost.

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Key Benefits:

• Objective metric: Reduces guesswork.
• Cost-effective: Avoid overspending on marginal perplexity gains.
• Performance balance: Aligns accuracy with latency.

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Real-World Use Cases:

• Chatbots: Pick the smallest model that meets perplexity targets.
• Summarization: Choose a model that balances coherence and speed.