On the Emergence and Test-Time Use of Structural Information in Large Language Models
Michelle Chao Chen, Moritz Miller, Bernhard Schölkopf ELLIS Institute and Max Planck Institute for Intelligent Systems, Max-Planck Institute, Siyuan Guo Prior Labs
Learning structural information from observational data is central to producing new knowledge outside the training corpus. This holds for mechanistic understanding in scientific discovery as well as flexible test-time compositional generation. We thus study how language models learn abstract structures and utilize the learnt structural information at test-time. To ensure a controlled setup, we design a natural language dataset based on linguistic structural transformations. We empirically show that the emergence of learning structural information correlates with complex reasoning tasks, and that the ability to perform test-time compositional generation remains limited.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表