Generalization Issues of Learnable Positional Embeddings

A language model is trained exclusively on text sequences with a maximum length of 512 tokens. This model uses a method where a unique vector is learned for each specific position in the sequence (e.g., a vector for position 1, a different vector for position 2, etc., up to position 512). After training is complete, the model is tasked with processing a new sequence that is 600 tokens long. What is the most direct and fundamental problem the model will encounter when processing the tokens from position 513 to 600?

Analysis of Positional Vector Assignment

A language model architect is designing a system to process sequences with a maximum length of 1024 tokens. They opt for an approach where a unique vector is created for each position (1, 2, ..., 1024). These vectors are initialized randomly and are updated based on the training objective, just like the other parameters in the model. Which statement best analyzes a key characteristic of this specific method for encoding position?

Limitation of Independent Positional Embeddings