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        <title>On the Emergence and Test-Time Use of Structural Information in Large Language Models</title>
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        <namePart type="given">Michelle</namePart>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T On the Emergence and Test-Time Use of Structural Information in Large Language Models
%A Chen, Michelle Chao
%A Miller, Moritz
%A Schölkopf, Bernhard
%A Guo, Siyuan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F chen-etal-2026-emergence
%X 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.
%U https://aclanthology.org/2026.acl-long.65/
%P 1449-1465

Markdown (Informal)

[On the Emergence and Test-Time Use of Structural Information in Large Language Models](https://aclanthology.org/2026.acl-long.65/) (Chen et al., ACL 2026)

• On the Emergence and Test-Time Use of Structural Information in Large Language Models (Chen et al., ACL 2026)

ACL

• Michelle Chao Chen, Moritz Miller, Bernhard Schölkopf, and Siyuan Guo. 2026. On the Emergence and Test-Time Use of Structural Information in Large Language Models. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1449–1465, San Diego, California, United States. Association for Computational Linguistics.