Across two languages and three metrics, no tested positional encoding scheme generalizes to code completion lengths unseen in training; mixed-length training is the recommended safe choice.
An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation,
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On the Generalizability of Transformer Models to Code Completions of Different Lengths
Across two languages and three metrics, no tested positional encoding scheme generalizes to code completion lengths unseen in training; mixed-length training is the recommended safe choice.