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In-context learning emerges across diverse AI modalities, study finds

By PulseAugur Editorial · [2 sources] ·

A new research paper proposes the Convergent Emergence Hypothesis, suggesting that few-shot in-context learning (ICL) capabilities, observed in large language models, may emerge broadly across different data modalities. The study developed a framework to test this hypothesis by applying tasks to six modalities: language, genome, integer sequences, time series, images, and proteins. Results indicate that ICL emerges across these modalities and shows correlated difficulty profiles in most, supporting the hypothesis in some domains. AI

IMPACT Suggests a common underlying structure for in-context learning across diverse AI modalities, potentially guiding future model development.

RANK_REASON The cluster contains a research paper published on arXiv and highlighted by Hugging Face, detailing a new hypothesis and experimental findings on in-context learning across multiple modalities.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

In-context learning emerges across diverse AI modalities, study finds

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The cluster contains a research paper published on arXiv and highlighted by Hugging Face, detailing a new hypothesis and experimental findings on in-context learning across multiple modalities.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Nathan Breslow, Seungwook Han, Daniel Hyunsoo Lee, Aayush Mishra, Anqi Liu, Daniel Khashabi ·

    Convergent Emergence of In-Context Learning Across Modalities

    arXiv:2609.14011v1 Announce Type: new 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…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Convergent Emergence of In-Context Learning Across Modalities

    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, …

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