内部短语还是外部短语？测试土耳其反身代词在适应型与思维链大型语言模型中的绑定
Clause-Internal or Clause-External? Testing Turkish Reflexive Binding in Adapted versus Chain of Thought Large Language Models
AI总结：
本研究通过对比两种大型语言模型，探讨土耳其反身代词在不同模型中的绑定行为差异，揭示模型架构和训练数据对指代依赖表示的影响。
摘要：
本研究评估了最先进的大型语言模型是否能够捕捉土耳其反身代词的绑定关系。我们构建了一个包含100个土耳其句子的平衡评估集，系统地将本地和非本地先行词进行对比，以测试反身代词kendi和kendisi。我们比较了两种对比系统：一个优化用于多步骤推理的OpenAI思维链模型和Trendyol-LLM-7B-base-v0.1，这是一个基于LLaMA 2衍生并大量在土耳其数据上微调的模型。先行词选择通过结合句子级困惑度与最小差异延续的强制选择比较来评估。总体而言，Trendyol-LLM在约70%的试验中倾向于本地绑定，表现出一致的局部偏好，这与对结构上更近的先行词的偏好一致。相比之下，OpenAI模型（o1 Mini）在本地和远距离解读之间几乎均等地分配其选择，表明在这一绑定配置中对局部性的敏感度较弱或不一致。总体来看，这些结果揭示了两种系统在绑定行为上的显著差异，并促使更深入地分析模型架构、训练数据和推理时的推理策略如何影响土耳其指代依赖的表示。
英文摘要：
This study evaluates whether state-of-the-art large language models capture the binding relations of Turkish reflexive pronouns. We construct a balanced evaluation set of 100 Turkish sentences that systematically pit local against non-local antecedents for the reflexives kendi and kendisi. We compare two contrasting systems: an OpenAI chain-of-thought model optimized for multi-step reasoning and Trendyol-LLM-7B-base-v0.1, a LLaMA 2 derived model extensively fine-tuned on Turkish data. Antecedent choice is assessed using a combined paradigm that integrates sentence-level perplexity with a forced-choice comparison between minimally differing continuations. Overall, Trendyol-LLM favors local bindings in approximately 70 percent of trials, exhibiting a robust locality bias consistent with a preference for structurally proximate antecedents. By contrast, the OpenAI model (o1 Mini) distributes its choices nearly evenly between local and long-distance readings, suggesting weaker or less consistent sensitivity to locality in this binding configuration. Taken together, these results reveal a marked contrast in binding behavior across the two systems and motivate closer analysis of how model architecture, training data, and inference-time reasoning strategies shape the representation of Turkish anaphoric dependencies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
