| Challenge: | Large Language Models (LLMs) perform well on a wide variety of tasks, authors say . they lack direct access to characters, which can be difficult to generalize to new languages . |
| Approach: | They propose a benchmark to test the orthographic knowledge of Large Language Models . they find that most LLMs seem to know the spelling of their tokens - yet fail to manipulate text . |
| Outcome: | The proposed benchmark tests the orthographic knowledge of large language models . it finds that most LLMs seem to know the spelling of their tokens, but fail to manipulate text . |
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| Challenge: | EXECUTE is an expandable X(Cross)-Lingual Extension of CUTE that can be expanded to any language. |
| Approach: | They extend the CUTE benchmark to more languages with diverse scripts and writing systems, introducing EXECUTE. |
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| Challenge: | Large language models (LLMs) can spell out tokens character by character with high accuracy, yet struggle with more complex character-level tasks. |
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SubTokenTest: A Practical Benchmark for Real-World Sub-token Understanding (2026.acl-long)
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| Challenge: | Existing benchmarks have highlighted character-level tasks as lacking practical relevance . many real-world applications rely heavily on precise sub-token understanding . |
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Phun-Bench: Evaluating LLMs on Phonological Understanding in Chinese (2026.acl-long)
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| Challenge: | Currently, many benchmarks evaluate the commonsense reasoning of large language models (LLMs), but most are English-based, limiting non-English evaluations. |
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Geng Zhang, Yizhou Ying, Sihang Jiang, Jiaqing Liang, Guanglei Yue, Yifei Fu, Hailin Hu, Yanghua Xiao
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Pun Unintended: LLMs and the Illusion of Humor Understanding (2025.emnlp-main)
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Alessandro Zangari, Matteo Marcuzzo, Andrea Albarelli, Mohammad Taher Pilehvar, Jose Camacho-Collados
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What do tokens know about their characters and how do they know it? (2022.naacl-main)
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| Challenge: | Pre-trained language models that use subword tokenization schemes can succeed at a variety of language tasks that require character-level information. |
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