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        <title>Phun-Bench: Evaluating LLMs on Phonological Understanding in Chinese</title>
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    <abstract>Language is a vehicle for thought, intricately tied to sounds, symbols, and meaning. However, most large language model (LLM) research focuses on meaning (semantics) and symbols (spelling) while largely overlooking sounds. Existing benchmarks on LLMs’ phonological abilities are either solvable through rote memorization or intertwined with other abilities, making them inadequate to measure LLMs’ genuine ability in *phonological understanding*. Here, we present Phun-Bench, a purpose-built Chinese benchmark with diverse tasks and settings across three dimensions (Homophony, Rhyme, and Phonetic Similarity), designed to systematically evaluate LLMs’ phonological understanding. Our results show that while LLMs excel at recalling correct pronunciations, they generally struggle to leverage phonological knowledge in the flexible and intuitive way that human speakers do. Moreover, through detailed analyses, we propose a hypothesis regarding the underlying mechanism of LLMs’ phonological understanding and “perception”, highlighting an underexplored frontier for future research.</abstract>
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%0 Conference Proceedings
%T Phun-Bench: Evaluating LLMs on Phonological Understanding in Chinese
%A Yue, Xing
%A Shen, Yongliang
%A Lu, Weiming
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F yue-etal-2026-phun
%X Language is a vehicle for thought, intricately tied to sounds, symbols, and meaning. However, most large language model (LLM) research focuses on meaning (semantics) and symbols (spelling) while largely overlooking sounds. Existing benchmarks on LLMs’ phonological abilities are either solvable through rote memorization or intertwined with other abilities, making them inadequate to measure LLMs’ genuine ability in *phonological understanding*. Here, we present Phun-Bench, a purpose-built Chinese benchmark with diverse tasks and settings across three dimensions (Homophony, Rhyme, and Phonetic Similarity), designed to systematically evaluate LLMs’ phonological understanding. Our results show that while LLMs excel at recalling correct pronunciations, they generally struggle to leverage phonological knowledge in the flexible and intuitive way that human speakers do. Moreover, through detailed analyses, we propose a hypothesis regarding the underlying mechanism of LLMs’ phonological understanding and “perception”, highlighting an underexplored frontier for future research.
%U https://aclanthology.org/2026.acl-long.1041/
%P 22723-22768

Markdown (Informal)

[Phun-Bench: Evaluating LLMs on Phonological Understanding in Chinese](https://aclanthology.org/2026.acl-long.1041/) (Yue et al., ACL 2026)

• Phun-Bench: Evaluating LLMs on Phonological Understanding in Chinese (Yue et al., ACL 2026)

ACL

• Xing Yue, Yongliang Shen, and Weiming Lu. 2026. Phun-Bench: Evaluating LLMs on Phonological Understanding in Chinese. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 22723–22768, San Diego, California, United States. Association for Computational Linguistics.