In English and other languages, multiple adjectives in noun phrases follow intricate ordering patterns. These patterns have been widely studied in linguistics and provide a useful test case for assessing how language models (LMs) acquire graded and context-sensitive word order preferences. We ask to what extent adjective order preferences in LMs can be explained by distributional learning alone, and where models exhibit behaviour that goes beyond surface co-occurrence patterns. We find that LM predictions are largely explained by training data frequencies: simple n-gram statistics account for much of their behaviour and closely mirror the preferences learned during training. However, by analysing learning dynamics we reveal that models also generalize robustly to unseen adjective combinations, indicating that their behaviour cannot be reduced to memorization of observed orders alone. Moreover, we show how LMs leverage word order cues from sentence context, demonstrating with feature attribution methods that contextual cues are an additional driver of adjective order in LM output.

翻译：在英语及其他语言中，名词短语中的多个形容词遵循复杂的排序规律。这些规律在语言学领域已被广泛研究，并为评估语言模型如何习得分级化、语境敏感的词序偏好提供了有效的测试案例。本研究旨在探究语言模型的形容词顺序偏好在多大程度上仅通过分布学习即可解释，以及模型在哪些方面表现出超越表层共现模式的行为。我们发现，语言模型的预测主要可通过训练数据频率解释：简单的n元语法统计特征能够解释其大部分行为，并紧密映射出训练过程中习得的偏好模式。然而，通过分析学习动态，我们揭示出模型对未见形容词组合同样表现出强大的泛化能力，这表明其行为不能简单归因于对已观测顺序的机械记忆。此外，我们通过特征归因方法证明，语言模型能够有效利用句子语境中的词序线索，表明语境提示是影响语言模型输出中形容词排序的附加驱动因素。