Title: Schema for In-Context Learning
Primary area: foundation or frontier models, including LLMs
Keywords: In-Context Learning, Schema Theory, Retrieval-Augmented Generation, Human-like Reasoning

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 structure understanding, we introduce SCHEMA-ACTIVATED IN-CONTEXT LEARNING (SA-ICL). This proposed framework extracts the representation of the Building Blocks of Cognition for the reasoning process instilled from prior examples, creating an abstracted schema — a lightweight, structured template of key inferential steps and their relationships — which is then used to augment a model’s reasoning process when presented with a novel question. We demonstrate that a broad range of large language models (LLMs) lack the capacity to form and utilize internal schema-based learning representations implicitly, but instead benefit significantly from explicit schema-based scaffolding. Across chemistry and physics questions from GPQA dataset, our empirical experiment results show that SA-ICL consistently boosts performance (up to 36.19%) when the single demonstration example is of high quality, which simultaneously reduces reliance on the number of demonstrations and enhances interpretability. SCHEMA-ACTIVATED IN-CONTEXT LEARNING not only bridges disparate ICL strategies ranging from pattern priming to Chain-of-Thought (CoT) prompting, but also paves a new path for enhancing human-like reasoning in LLMs.

TL;DR: A schema-activated framework for in-context learning that enhances human-like reasoning.