Large language models achieve strong performance on many language tasks, yet it remains unclear whether they integrate world knowledge with syntactic structure in a human-like, structure-sensitive way during ambiguity resolution. We test this question in Turkish prenominal relative-clause attachment ambiguities, where the same surface string permits high attachment (HA) or low attachment (LA). We construct ambiguous items that keep the syntactic configuration fixed and ensure both parses remain pragmatically possible, while graded event plausibility selectively favors High Attachment vs.\ Low Attachment. The contrasts are validated with independent norming ratings. In a speeded forced-choice comprehension experiment, humans show a large, correctly directed plausibility effect. We then evaluate Turkish and multilingual LLMs in a parallel preference-based setup that compares matched HA/LA continuations via mean per-token log-probability. Across models, plausibility-driven shifts are weak, unstable, or reversed. The results suggest that, in the tested models, plausibility information does not guide attachment preferences as reliably as it does in human judgments, and they highlight Turkish RC attachment as a useful cross-linguistic diagnostic beyond broad benchmarks.

翻译：中文摘要：大语言模型在许多语言任务上表现出色，但在消解歧义时能否以类似人类的结构敏感方式将世界知识与句法结构整合仍不清楚。我们以土耳其语前关系从句附着歧义为测试案例，其中相同的表层字符串允许高附着或低附着。我们构建了保持句法配置固定且确保两种解析在语用上均可行的歧义项目，同时通过分级事件合理性选择性地支持高附着或低附着。通过独立规范评级验证了这种对比。在限时强制选择理解实验中，人类表现出显著且方向正确的合理性效应。随后，我们在基于偏好的平行设置下评估了土耳其语和多语言大语言模型，通过平均每词对数概率比较匹配的高/低附着延续。各模型中，合理性驱动的偏好变化微弱、不稳定甚至出现反转。结果表明，在测试模型中，合理性信息引导附着偏好的可靠性不及人类判断，并凸显出土耳其语关系从句附着作为超越广泛基准的跨语言诊断工具的价值。