@inproceedings{gambardella-etal-2025-inconsistent,
title = "Inconsistent Tokenizations Cause Language Models to be Perplexed by {J}apanese Grammar",
author = "Gambardella, Andrew and
Kojima, Takeshi and
Iwasawa, Yusuke and
Matsuo, Yutaka",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-short.75/",
doi = "10.18653/v1/2025.acl-short.75",
pages = "970--976",
ISBN = "979-8-89176-252-7",
abstract = "Typical methods for evaluating the performance of language models evaluate their ability to answer questions accurately. These evaluation metrics are acceptable for determining the extent to which language models can understand and reason about text in a general sense, but fail to capture nuanced capabilities, such as the ability of language models to recognize and obey rare grammar points, particularly in languages other than English. We measure the perplexity of language models when confronted with the ``first person psych predicate restriction'' grammar point in Japanese. Weblab is the only tested open source model in the 7-10B parameter range which consistently assigns higher perplexity to ungrammatical psych predicate sentences than grammatical ones. We give evidence that Weblab{'}s uniformly bad tokenization is a possible root cause for its good performance, and show that Llama 3{'}s perplexity on grammatical psych predicate sentences can be reduced by orders of magnitude (28x difference) by restricting test sentences to those with uniformly well-behaved tokenizations. We show in further experiments on machine translation tasks that language models will use alternative grammar patterns in order to produce grammatical sentences when tokenization issues prevent the most natural sentence from being output."
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<abstract>Typical methods for evaluating the performance of language models evaluate their ability to answer questions accurately. These evaluation metrics are acceptable for determining the extent to which language models can understand and reason about text in a general sense, but fail to capture nuanced capabilities, such as the ability of language models to recognize and obey rare grammar points, particularly in languages other than English. We measure the perplexity of language models when confronted with the “first person psych predicate restriction” grammar point in Japanese. Weblab is the only tested open source model in the 7-10B parameter range which consistently assigns higher perplexity to ungrammatical psych predicate sentences than grammatical ones. We give evidence that Weblab’s uniformly bad tokenization is a possible root cause for its good performance, and show that Llama 3’s perplexity on grammatical psych predicate sentences can be reduced by orders of magnitude (28x difference) by restricting test sentences to those with uniformly well-behaved tokenizations. We show in further experiments on machine translation tasks that language models will use alternative grammar patterns in order to produce grammatical sentences when tokenization issues prevent the most natural sentence from being output.</abstract>
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%0 Conference Proceedings
%T Inconsistent Tokenizations Cause Language Models to be Perplexed by Japanese Grammar
%A Gambardella, Andrew
%A Kojima, Takeshi
%A Iwasawa, Yusuke
%A Matsuo, Yutaka
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-252-7
%F gambardella-etal-2025-inconsistent
%X Typical methods for evaluating the performance of language models evaluate their ability to answer questions accurately. These evaluation metrics are acceptable for determining the extent to which language models can understand and reason about text in a general sense, but fail to capture nuanced capabilities, such as the ability of language models to recognize and obey rare grammar points, particularly in languages other than English. We measure the perplexity of language models when confronted with the “first person psych predicate restriction” grammar point in Japanese. Weblab is the only tested open source model in the 7-10B parameter range which consistently assigns higher perplexity to ungrammatical psych predicate sentences than grammatical ones. We give evidence that Weblab’s uniformly bad tokenization is a possible root cause for its good performance, and show that Llama 3’s perplexity on grammatical psych predicate sentences can be reduced by orders of magnitude (28x difference) by restricting test sentences to those with uniformly well-behaved tokenizations. We show in further experiments on machine translation tasks that language models will use alternative grammar patterns in order to produce grammatical sentences when tokenization issues prevent the most natural sentence from being output.
%R 10.18653/v1/2025.acl-short.75
%U https://aclanthology.org/2025.acl-short.75/
%U https://doi.org/10.18653/v1/2025.acl-short.75
%P 970-976
Markdown (Informal)
[Inconsistent Tokenizations Cause Language Models to be Perplexed by Japanese Grammar](https://aclanthology.org/2025.acl-short.75/) (Gambardella et al., ACL 2025)
ACL