k context of k. Even - with architecture -
Posted: Mon Dec 23, 2024 10:38 am
. Short context: In the k context, -, and have comparable performance lines with mostly overlapping lines. - performs slightly worse at larger budgets. Although - has better complexity than - at each model size, the additional cost of - offsets this advantage. In the k context, both - and - perform significantly better than . Even - with architecture - performs slightly better than . In addition, the researchers observed a very obvious phenomenon: as the context length becomes longer, the advantage of the layer relative to becomes greater. . Long context: k In order to evaluate the function in long context, the researchers used a popular subset - k to experiment with context lengths from k to k increments.
As can be observed from the above figure, - has similar japan mobile number results in the context. On a . scale, - is only slightly worse than -. Due to the lack of a clear linear fit, it is difficult to derive an empirical scaling law. However, the strong trend of - suggests that the architecture may be more suitable for larger models and longer contexts beyond the evaluation. Context length as a hyperparameter Although the length of the input sequence is determined by the user, the context length of the language model processing the input can be determined by the engineer.
Therefore, the context length is also a selectable hyperparameter. For those with linear complexityThe observation still holds true with the only exception that performs slightly better than -. In the k context, both - and - outperform the observation with k. The researchers chose the perplexity of because each context has the same length. 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 mostly overlap after ^. performs significantly better than because it benefits from long contexts without incurring a huge cost in training.
As can be observed from the above figure, - has similar japan mobile number results in the context. On a . scale, - is only slightly worse than -. Due to the lack of a clear linear fit, it is difficult to derive an empirical scaling law. However, the strong trend of - suggests that the architecture may be more suitable for larger models and longer contexts beyond the evaluation. Context length as a hyperparameter Although the length of the input sequence is determined by the user, the context length of the language model processing the input can be determined by the engineer.
Therefore, the context length is also a selectable hyperparameter. For those with linear complexityThe observation still holds true with the only exception that performs slightly better than -. In the k context, both - and - outperform the observation with k. The researchers chose the perplexity of because each context has the same length. 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 mostly overlap after ^. performs significantly better than because it benefits from long contexts without incurring a huge cost in training.