Papers by Moritz Miller

2 papers
On the Emergence and Test-Time Use of Structural Information in Large Language Models (2026.acl-long)

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Challenge: a controlled environment is required to study how language models learn structural information from observational data.
Approach: They propose a natural language dataset based on linguistic structural transformations to study how language models learn abstract structures and utilize the learnt structural information at test-time.
Outcome: The proposed model can generate new knowledge outside the training corpus in a controlled environment.
First-Step Advantage: Importance of Starting Right in Multi-Step Math Reasoning (2025.findings-acl)

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Challenge: Language models can solve complex reasoning tasks better by learning to generate rationales for their predictions.
Approach: They propose to use a larger model to guide smaller models to start . this allows them to generate rationales for their predictions when correct .
Outcome: The proposed method improves performance on multistep reasoning datasets over multiple smaller models.

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