Understanding Perplexity in Language Models: A Key Metric | Sam Ghosh posted on the topic | LinkedIn

🔍 Understanding Perplexity in Language Models When we talk about evaluating language models, one metric comes up again and again: Perplexity. 👉 What it means: Perplexity measures how well a model predicts the next token in a sequence. Lower perplexity = better predictions. In simple terms, it reflects the average number of choices a model considers when generating text. 👉 Why it matters: Model Size → Bigger models usually mean lower perplexity. Training Data → Diverse, high-quality data impr…