@inproceedings{zuo-etal-2025-position,
title = "Position Information Emerges in Causal Transformers Without Positional Encodings via Similarity of Nearby Embeddings",
author = "Zuo, Chunsheng and
Guerzhoy, Pavel and
Guerzhoy, Michael",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.632/",
pages = "9418--9430",
abstract = "Transformers with causal attention can solve tasks that require positional information without using positional encodings. In this work, we propose and investigate a new hypothesis about how positional information can be stored without using explicit positional encoding. We observe that nearby embeddings are more similar to each other than faraway embeddings, allowing the transformer to potentially reconstruct the positions of tokens. We show that this pattern can occur in both the trained and the randomly initialized Transformer models with causal attention and no positional encodings over a common range of hyperparameters."
}
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<abstract>Transformers with causal attention can solve tasks that require positional information without using positional encodings. In this work, we propose and investigate a new hypothesis about how positional information can be stored without using explicit positional encoding. We observe that nearby embeddings are more similar to each other than faraway embeddings, allowing the transformer to potentially reconstruct the positions of tokens. We show that this pattern can occur in both the trained and the randomly initialized Transformer models with causal attention and no positional encodings over a common range of hyperparameters.</abstract>
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%0 Conference Proceedings
%T Position Information Emerges in Causal Transformers Without Positional Encodings via Similarity of Nearby Embeddings
%A Zuo, Chunsheng
%A Guerzhoy, Pavel
%A Guerzhoy, Michael
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F zuo-etal-2025-position
%X Transformers with causal attention can solve tasks that require positional information without using positional encodings. In this work, we propose and investigate a new hypothesis about how positional information can be stored without using explicit positional encoding. We observe that nearby embeddings are more similar to each other than faraway embeddings, allowing the transformer to potentially reconstruct the positions of tokens. We show that this pattern can occur in both the trained and the randomly initialized Transformer models with causal attention and no positional encodings over a common range of hyperparameters.
%U https://aclanthology.org/2025.coling-main.632/
%P 9418-9430
Markdown (Informal)
[Position Information Emerges in Causal Transformers Without Positional Encodings via Similarity of Nearby Embeddings](https://aclanthology.org/2025.coling-main.632/) (Zuo et al., COLING 2025)
ACL