Long context: k To evaluate the functionality in long contexts, the researchers used a popular subset, k, and experimented with context lengths in increments from k to k. As can be observed from the figure above, the results for all k in the k contexts of k are similar. Even with the architecture, - performs slightly better than - in the k context. makes it difficult to derive empirical scaling laws. However, the strong trend of - suggests that the architecture may be better suited for larger models and longer contexts beyond the evaluation.
Context length as a hyperparameter While the length of the lithuania phone code input sequence is determined by the user, the length of the context in which the language model processes the input can be determined by the engineer. Therefore, context length is also a hyperparameter that can be chosen. For the observations with linear multiplexing, the results still hold, with the only exception that - performs slightly better than -. In k contexts, both - and - outperform the observations with k. The researchers chose the perplexity of because each context length has the same. From the figure, we can observe the following results- The lines of the best performing methods - and - overlap almost completely.
The lines of and also overlap mostly after ^. The performance of is significantly better than because it benefits from long contexts without incurring a huge cost in training. For all methods trained from scratch (including pre-training), the perplexity gets worse once the context length becomes too large. From the above figure, we can see that compared with -, - performs slightly worse in short contexts but performs better in long contexts. This observation is in line with the researchers' expectation that as a hidden state, is more expressive than the linear model. Again, all methods have the same training as.