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：大型语言模型在许多语言任务上表现出色，但尚不清楚它们在歧义消解过程中，是否能以类似人类、对结构敏感的方式，将世界知识与句法结构整合。我们以土耳其语前指关系从句附着歧义为测试对象——在该语言中，同一表层字符串允许高位附着或低位附着。我们构建了保持句法配置固定、并确保两种解析在语用上均可能的歧义项，同时通过分级事件合理性选择性偏向高位附着或低位附着。这些对比经独立规范评级验证。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，我们在平行偏好设置中评估了土耳其语及多语言大型语言模型——该设置通过逐词平均对数概率比较匹配的高位附着/低位附着续接。在各模型中，由合理性驱动的偏好转变微弱、不稳定甚至反向。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，并突显出土耳其语关系从句附着作为超越宽泛基准的跨语言诊断工具的效用。