Phonology, the study of speech's structure and pronunciation rules, is a critical yet often overlooked component in Large Language Model (LLM) research. LLMs are widely used in various downstream applications that leverage phonology such as educational tools and poetry generation. Moreover, LLMs can potentially learn imperfect associations between orthographic and phonological forms from the training data. Thus, it is imperative to benchmark the phonological skills of LLMs. To this end, we present PhonologyBench, a novel benchmark consisting of three diagnostic tasks designed to explicitly test the phonological skills of LLMs in English: grapheme-to-phoneme conversion, syllable counting, and rhyme word generation. Despite having no access to speech data, LLMs showcased notable performance on the PhonologyBench tasks. However, we observe a significant gap of 17% and 45% on Rhyme Word Generation and Syllable counting, respectively, when compared to humans. Our findings underscore the importance of studying LLM performance on phonological tasks that inadvertently impact real-world applications. Furthermore, we encourage researchers to choose LLMs that perform well on the phonological task that is closely related to the downstream application since we find that no single model consistently outperforms the others on all the tasks.

翻译：音韵学作为研究语音结构与发音规则的学科，在大语言模型研究中至关重要却常被忽视。大语言模型广泛应用于教育工具、诗歌生成等依赖音韵学的下游任务。由于模型可能从训练数据中习得不完善的正字法与音韵形式对应关系，因此亟需建立音韵学能力的评估基准。为此，我们提出PhonologyBench——一个包含三项诊断任务的新型基准测试，旨在系统评估大语言模型在英语中的音韵学能力：字素-音素转换、音节计数与押韵词生成。尽管缺乏语音数据输入，大语言模型在PhonologyBench任务中仍展现出显著性能。然而，与人类表现相比，模型在押韵词生成和音节计数任务上分别存在17%和45%的显著差距。研究结果凸显了评估大语言模型音韵任务表现的重要性——这种能力会间接影响实际应用效果。基于"无单一模型能在所有任务上持续领先"的发现，我们建议研究者优先选择在与下游应用密切相关的音韵任务中表现优异的大语言模型。