Convergent Emergence of In-Context Learning Across Modalities

2026-09-12

AI Summary

研究通过跨模态框架测试不同领域中少样本情境学习（ICL）的共通性，发现ICL在六个模态中出现，并且在多数模态间具有相关效应。

Abstract

Few-shot in-context learning (ICL), the capacity of a model to infer abstract patterns from input-output examples provided in its prompt and apply them to new inputs, has been extensively studied in large language models trained for next-token prediction on human text. Recently, few-shot ICL has been demonstrated in autoregressive genomic models as well. This raises a question: does ICL emerge broadly across domains, and if so, what common structure is shared? To address both, we develop a controlled cross-modality framework that instantiates the same task suite in a variety of modalities to test what we call the Convergent Emergence Hypothesis: the idea that few-shot ICL, when it emerges, shares a common cross-modality difficulty profile - i.e., tasks that benefit from ICL in one modality tend to benefit in others. We show that paired-mapping ICL emerges across six modalities (language, genome, integer sequences, time series, images, and proteins), surpasses controlled baselines, and has correlated per-task effects across five of them. Together, these results provide support for the Convergent Emergence Hypothesis in some modalities, but not all.

Problem

• in-context learning
• few-shot
• cross-modality
• convergent emergence

Innovation

• few-shot in-context learning
• cross-modality framework
• Convergent Emergence Hypothesis