Evaluating LLMs on Chinese Topic Constructions: A Research Proposal Inspired by Tian et al. (2024) Xiaodong Yang College of Foreign Languages Zhejiang University o f Technology 288, Liuhe Rd., Xihu District, Hangzhou, Zhejiang, 310023 China x dyang @zjut.edu.cn Abstract This paper proposes a framework for evaluating large language models (LLMs) on Chinese topic constructions, focusing on their sensitivity to island constraints. Drawing inspiration from Tian et al. (2024), we outline an experimental design for testing LLMs' grammatical knowledge of Mandarin syntax. While no experiments have been conducted yet, this proposal aims to provide a foundation for future studies and invites feedback on the methodology. K eywords: LLM behavior, topic construction, experimental syntax 1. Introduction C hinese is well known as a topic - prominent language, where the sentence - initial topic plays a central role in structuring information (Li & Thompson, 1981).

Humans develop their grammars by making structural generalizations from finite input. We ask how filler-gap dependencies, which share a structural generalization despite diverse surface forms, might arise from the input. We explicitly control the input to a neural language model (NLM) to uncover whether the model posits a shared representation for filler-gap dependencies. We show that while NLMs do have success differentiating grammatical from ungrammatical filler-gap dependencies, they rely on superficial properties of the input, rather than on a shared generalization. Our work highlights the need for specific linguistic inductive biases to model language acquisition.