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Causal transformer language models (LMs), such as GPT-3, typically requiresome form of positional encoding, such as positional embeddings. However, weshow that LMs without any explicit positional encoding are still competitivewith standard models, and that this phenomenon is robust across differentdatasets, model sizes, and sequence lengths. Probing experiments reveal thatsuch models acquire an implicit notion of absolute positions throughout thenetwork, effectively compensating for the missing information. We conjecturethat causal attention enables the model to infer the number of predecessorsthat each token can attend to, thereby approximating its absolute position. Ourfindings indicate that causal LMs might derive positional awareness not onlyfrom the explicit positioning mechanism, but also from the effects of thecausal mask.

Transformer Language Models without Positional Encodings Still Learn Positional Information