December 01, 2022
Transformer language models encode the notion of word order using positional information. Most commonly, this positional information is represented by absolute position embeddings (APEs), that are learned from the pretraining data. However, in natural language, it is not absolute position that matters, but relative position, and the extent to which APEs can capture this type of information has not been investigated. In this work, we observe that models trained with APE over-rely on positional in- formation to the point that they break-down when subjected to sentences with shifted posi- tion information. Specifically, when models are subjected to sentences starting from a non-zero position (excluding the effect of priming), they exhibit noticeably degraded performance on zero- to full-shot tasks, across a range of model families and model sizes. Our findings raise questions about the efficacy of APEs to model the relativity of position information, and invite further introspection on the sentence and word order processing strategies employed by these models.
Publisher
EMNLP
February 07, 2025
The Omnilingual MT Team, Pierre Andrews, Mikel Artetxe, Mariano Coria Meglioli, Marta R. Costa-jussa, Joe Chuang, David Dale, Cynthia Gao, Jean Maillard, Alexandre Mourachko, Christophe Ropers, Safiyyah Saleem, Eduardo Sánchez, Yiannis Tsiamas, Arina Turkatenko, Albert Ventayol, Shireen Yates
February 07, 2025
February 06, 2025
Jarod Levy, Mingfang (Lucy) Zhang, Svetlana Pinet, Jérémy Rapin, Hubert Jacob Banville, Stéphane d'Ascoli, Jean Remi King
February 06, 2025
February 06, 2025
Mingfang (Lucy) Zhang, Jarod Levy, Stéphane d'Ascoli, Jérémy Rapin, F.-Xavier Alario, Pierre Bourdillon, Svetlana Pinet, Jean Remi King
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January 04, 2025
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