Logits

In depth

The raw, unnormalized scores a language model's output layer assigns to every token in its vocabulary at each step, before softmax turns them into probabilities. Higher logits mean the model considers that token more likely to come next. Sampling controls like temperature, top-p, repetition penalties, and logit bias all work by modifying logits before a token is picked. Some classifiers built on LLMs read the logits of just a few tokens, such as yes and no, to get a confidence score.

Primary source: huggingface.co