语言模型作为人工学习者：探讨跨语言影响
A Study of Crosslinguistic Influence in Language Models
AI总结：
本研究利用语言模型探讨跨语言影响，发现语言主导性和熟练度对CLI有显著影响，且L1在L2处理中被共激活。
摘要：
尽管跨语言影响（CLI）在双语研究中至关重要，但人类研究往往由于固有的实验变异性而产生矛盾的结果。我们通过使用语言模型（LMs）作为受控的统计学习者，系统地模拟CLI并隔离其潜在驱动因素来解决这些不一致。具体而言，我们研究了改变L1语言主导性和L2语言熟练度的影响，我们通过控制L2的接触年龄——定义为L2引入训练步骤来操纵。此外，我们还探讨了在不同句法距离的L1语言上预训练对CLI的影响。通过跨语言启动，我们分析了激活L1结构如何影响L2处理。我们的结果与心理语言学研究的证据一致，证实了语言主导性和熟练度是CLI的强大预测因素。我们进一步发现，尽管语法结构的启动是双向的，但非语法结构的启动则对语言主导性敏感。最后，我们提供了CLI在LMs中的机制证据，证明在L2处理过程中L1是共激活的，并且直接影响L2所招募的神经回路。更广泛地说，我们的工作表明，LMs可以作为计算框架来指导人类CLI理论。
英文摘要：
The sequential acquisition of languages inevitably leads to Crosslinguistic Influence (CLI), where the syntactic properties of a first language (L1) impact the processing of a second language (L2). While modern language models exhibit robust cross-lingual transfer, the exact mechanisms governing how language dominance, relative proficiency, and typological distance dictate structural interference warrant deeper investigation. In this work, we systematically investigate CLI in artificial learners by training simultaneous and sequential bilingual models across 15 typologically diverse L1s and varying the Step of Exposure (SoE), defined as the specific training step at which the L2 is introduced. Utilizing crosslinguistic structural priming, we decouple latent CLI into distinct positive and negative transfer rates. Our evaluations reveal a critical computational tradeoff: while increased L1 dominance (higher SoE) strongly amplifies the correlation between structural transfer and syntactic distance, diminished L2 proficiency bottlenecks the model's capacity for positive transfer, leaving it highly vulnerable to persistent negative interference from distant L1s. Mechanistically, we demonstrate that L1 typological proximity physically dictates the cross-lingual overlap of L2 syntactic neurons. Furthermore, we uncover that explicit priming induces a dynamic layer-wise migration of grammatical resolution to the terminal layers of the network. Through targeted causal ablations, we confirm that deep-layer attention mechanisms exclusively drive this crosslinguistic transfer by routing the L1 structural prior into the final prediction. Ultimately, our findings demonstrate that CLI in language models is not an arbitrary artifact of capacity constraints, but a structured phenomenon fundamentally governed by the interplay of language dominance and proficiency.
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