Decomposing how prompting steers behavior
The paper titled 'Decomposing how prompting steers behavior' explores how prompting influences the internal representations of large language models and vision-language models. It introduces a geometric decomposition framework to analyze how different prompts reshape these representations and affect behavior. The findings suggest that prompts significantly alter representations toward the instructed task structure, with specific transformations being more effective in achieving behavioral alignment.
- ▪The study focuses on how prompting affects the internal representations of language and vision models.
- ▪A nested geometric decomposition framework is introduced to analyze the effects of prompting.
- ▪The research shows that prompts consistently reshape representations toward the instructed task structure.
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Computer Science > Artificial Intelligence arXiv:2606.03093 (cs) [Submitted on 2 Jun 2026] Title:Decomposing how prompting steers behavior Authors:Fan L. Cheng, Nikolaus Kriegeskorte View a PDF of the paper titled Decomposing how prompting steers behavior, by Fan L. Cheng and Nikolaus Kriegeskorte View PDF HTML (experimental) Abstract:Prompting steers large language models (LLMs) and vision-language models (VLMs) without weight updates, but it remains unclear how instruction changes reshape internal representations to produce behavior. We introduce a nested geometric decomposition framework that treats prompting as a transformation of the representational geometry of the content following the prompt.
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