Classification of Generalization Approaches for Positional Embeddings

Positional Encoding without Generalization

A team trains a language model using an architecture where a unique vector is learned for every possible token position. The entire training dataset consists of texts that are no longer than 1,024 tokens. After training, the model shows excellent performance on all evaluation texts that are 1,024 tokens or shorter. However, when deployed to process a new, 1,500-token document, the model's ability to understand relationships between words degrades dramatically, particularly for words appearing af

Explaining Extrapolation Failure in Positional Embeddings

Evaluating a Flawed Generalization Strategy

Generalizable Positional Embeddings