发表机构
Seoul National University（首尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究探究语言模型对音系信息的表征与使用，以英语不定冠词a/an为例揭示其通过线性方向编码音系条件并驱动选择，还验证类规则泛化的延伸性，为语素变体选择提供机理解释。
AI 中文摘要
语言模型在分词文本上进行训练，而分词文本会掩盖词语的声音结构，但它们仍能可靠地生成形式受音系条件制约的语素。目前尚不清楚它们是依赖针对特定项目的记忆，还是类规则的泛化，若是后者，这种泛化是如何实现的。因此，我们探究这种音系条件是否在语言模型中得到表征，以及它如何因果性地用于语素变体选择。针对英语不定冠词a/an，我们表明音系条件在触发词嵌入中沿单一线性方向编码，该方向在词级wug测试中因果性地驱动冠词选择，且在冠词预测位置，模型会预测即将出现的触发词，并利用预测触发词的音系特征选择冠词。随后，我们探究这种类规则泛化是否超出英语冠词选择的范围，延伸至其他语言的语素变体选择和明确的音系判断。这些结果共同为语言模型中受音系条件制约的语素变体选择提供了机理解释，并将这种生成时的能力与明确的元语言判断分离开来。
英文摘要
Language models are trained on tokenized text that obscures the sound structure of words, yet they reliably produce morphemes whose form is phonologically conditioned. It remains unclear whether they rely on item-specific memorization or rule-like generalization and, if the latter, how that generalization is implemented. We therefore ask whether this phonological condition is represented within language models and how it is causally used for allomorph selection. For the English indefinite article a/an, we show that the phonological condition is encoded along a single linear direction in trigger-token embeddings, that this direction causally drives article selection in token-level wug tests, and that, at the article-prediction position, the model forecasts the upcoming trigger token and uses the forecasted trigger's phonological feature to choose the article. We then ask whether this rule-like generalization extends beyond English article selection, both to allomorph selection in other languages and to explicit phonological judgment. Together, these results provide a mechanistic account of phonologically conditioned allomorph selection in language models, and dissociate this generation-time ability from explicit metalinguistic judgments.
CommentsAccepted to EMNLP 2026
