Schema for In-Context Learning

📰 ArXiv cs.AI

Researchers propose a schema-based approach for in-context learning to improve knowledge retrieval and transfer in transformer-based language models

advanced Published 31 Mar 2026
Action Steps
  1. Identify pre-existing mental frameworks (schemas) that can be applied to new tasks
  2. Develop explicit modules for knowledge retrieval and transfer at the abstraction level
  3. Integrate schema theory into transformer-based language models to enable in-context learning
  4. Evaluate the performance of schema-based in-context learning models on various tasks
Who Needs to Know This

AI engineers and ML researchers can benefit from this approach to develop more efficient and effective in-context learning models, while data scientists can apply this knowledge to improve language model performance

Key Insight

💡 Schema theory can be applied to improve knowledge retrieval and transfer in in-context learning

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💡 Schema-based in-context learning for transformer-based language models

Key Takeaways

Researchers propose a schema-based approach for in-context learning to improve knowledge retrieval and transfer in transformer-based language models

Full Article

Title: Schema for In-Context Learning

Abstract:
arXiv:2510.13905v3 Announce Type: replace-cross Abstract: In-Context Learning (ICL) enables transformer-based language models to adapt to new tasks by conditioning on demonstration examples. However, traditional example-driven in-context learning lacks explicit modules for knowledge retrieval and transfer at the abstraction level. Inspired by cognitive science, specifically schema theory, which holds that humans interpret new information by activating pre-existing mental frameworks (schemas) to
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