The broader spectrum of in-context learning

2024-12-05

arXiv.org

AI Summary

This paper addresses the challenge of unifying diverse in-context learning (ICL) phenomena in large language models (LLMs)—including instruction following, role-playing, and temporal extrapolation—under a coherent theoretical framework. Method: We recast ICL as a meta-learning process: any context that nontrivially reduces subsequent prediction loss constitutes generalized ICL. Introducing the “ICL broad-spectrum view,” we integrate sequence distribution analysis with meta-learning theory to link ICL to foundational linguistic capabilities (e.g., coreference resolution, parallel structure processing) and systematically define multidimensional generalization metrics. Contribution/Results: We establish ICL as a meta-learning–driven universal adaptation mechanism—the first such unified theoretical perspective. Our framework clarifies distinct axes of generalization (e.g., task, domain, structural) and strengthens conceptual connections between ICL and emerging paradigms such as goal-directed agentic behavior. This advances both theoretical understanding and principled evaluation of LLM adaptation.

Technology Category

Machine Learning: Large Multimodal Models (LMMs)Natural Language Processing: (Large) Language ModelsSearch and Optimization: Metareasoning and Metaheuristics

Application Category

User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendationSemantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsSearch and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

The ability of language models to learn a task from a few examples in context has generated substantial interest. Here, we provide a perspective that situates this type of supervised few-shot learning within a much broader spectrum of meta-learned in-context learning. Indeed, we suggest that any distribution of sequences in which context non-trivially decreases loss on subsequent predictions can be interpreted as eliciting a kind of in-context learning. We suggest that this perspective helps to unify the broad set of in-context abilities that language models exhibit $unicode{x2014}$ such as adapting to tasks from instructions or role play, or extrapolating time series. This perspective also sheds light on potential roots of in-context learning in lower-level processing of linguistic dependencies (e.g. coreference or parallel structures). Finally, taking this perspective highlights the importance of generalization, which we suggest can be studied along several dimensions: not only the ability to learn something novel, but also flexibility in learning from different presentations, and in applying what is learned. We discuss broader connections to past literature in meta-learning and goal-conditioned agents, and other perspectives on learning and adaptation. We close by suggesting that research on in-context learning should consider this broader spectrum of in-context capabilities and types of generalization.

Problem

• Studying broader spectrum of meta-learned in-context learning
• Unifying diverse in-context abilities language models exhibit
• Investigating generalization dimensions beyond novel learning capability

Innovation

• Meta-learned in-context learning spectrum
• Generalization across multiple dimensions study
• Contextual sequence distribution interpretation approach