用语言模型探查语言的可能与不可能，揭示人类语言学习的内在机制。
Language models as tools for investigating the distinction between possible and impossible natural languages
- 通过迭代优化语言模型架构，区分语言的可实现性
- 构建可解释框架，关联语言特征与人类认知假设
- 适合研究语言习得、认知科学与生成模型交叉领域的学者
我们认为语言模型（LM）在探究自然语言的可能与不可能之间具有强大潜力，有助于揭示支持人类语言学习的归纳偏置。我们提出一个分阶段的研究计划，通过迭代改进语言模型架构，使其更精准地区分可能与不可能的语言，从而支持将语言假说与人类认知联系起来。
原文摘要 · Abstract (English)
We argue that language models (LMs) have strong potential as investigative tools for probing the distinction between possible and impossible natural languages and thus uncovering the inductive biases that support human language learning. We outline a phased research program in which LM architectures are iteratively refined to better discriminate between possible and impossible languages, supporting linking hypotheses to human cognition.
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