@inproceedings{cosma-etal-2025-strawberry,
title = "The Strawberry Problem: Emergence of Character-level Understanding in Tokenized Language Models",
author = "Cosma, Adrian and
Ruseti, Stefan and
Radoi, Emilian and
Dascalu, Mihai",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1434/",
doi = "10.18653/v1/2025.emnlp-main.1434",
pages = "28252--28263",
ISBN = "979-8-89176-332-6",
abstract = "Despite their remarkable progress across diverse domains, Large Language Models (LLMs) consistently fail at simple character-level tasks, such as counting letters in words, due to a fundamental limitation: tokenization. In this work, we frame this limitation as a problem of low mutual information and analyze it in terms of concept emergence. Using a suite of 19 synthetic tasks that isolate character-level reasoning in a controlled setting, we show that such capabilities emerge suddenly and only late in training. We find that percolation-based models of concept emergence explain these patterns, suggesting that learning character composition is not fundamentally different from learning commonsense knowledge. To address this bottleneck, we propose a lightweight architectural modification that significantly improves character-level reasoning while preserving the inductive advantages of subword models. Together, our results bridge low-level perceptual gaps in tokenized LMs and provide a principled framework for understanding and mitigating their structural blind spots. We make our code publicly available."
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%0 Conference Proceedings
%T The Strawberry Problem: Emergence of Character-level Understanding in Tokenized Language Models
%A Cosma, Adrian
%A Ruseti, Stefan
%A Radoi, Emilian
%A Dascalu, Mihai
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F cosma-etal-2025-strawberry
%X Despite their remarkable progress across diverse domains, Large Language Models (LLMs) consistently fail at simple character-level tasks, such as counting letters in words, due to a fundamental limitation: tokenization. In this work, we frame this limitation as a problem of low mutual information and analyze it in terms of concept emergence. Using a suite of 19 synthetic tasks that isolate character-level reasoning in a controlled setting, we show that such capabilities emerge suddenly and only late in training. We find that percolation-based models of concept emergence explain these patterns, suggesting that learning character composition is not fundamentally different from learning commonsense knowledge. To address this bottleneck, we propose a lightweight architectural modification that significantly improves character-level reasoning while preserving the inductive advantages of subword models. Together, our results bridge low-level perceptual gaps in tokenized LMs and provide a principled framework for understanding and mitigating their structural blind spots. We make our code publicly available.
%R 10.18653/v1/2025.emnlp-main.1434
%U https://aclanthology.org/2025.emnlp-main.1434/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1434
%P 28252-28263
Markdown (Informal)
[The Strawberry Problem: Emergence of Character-level Understanding in Tokenized Language Models](https://aclanthology.org/2025.emnlp-main.1434/) (Cosma et al., EMNLP 2025)
ACL