To measure the quality of a language model and make performance comparable across documents of different lengths, we evaluate it using the cross-entropy loss averaged over all $n$ tokens in a sequence. The formula is given by: $\frac{1}{n} \sum_{t=1}^n -\log P(x_t \mid x_{t-1}, \ldots, x_1)$, where $P$ represents the conditional probability provided by the language model and $x_t$ is the actual token observed at time step $t$. A better model yields a lower average loss, which conceptually corresponds to spending fewer bits to compress the sequence.