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Unlocking In-Context Learning for Natural Datasets Beyond Language Modeling
Created by
Haebom
Author
Jelena Bratuli c, Sudhanshu Mittal, David T. Hoffmann, Samuel B ohm, Robin Tibor Schirrmeister, Tonio Ball, Christian Rupprecht, Thomas Brox
Outline
This paper studies the extension of the context-based learning (ICL) capability of large-scale language models (LLMs) to various modalities (other than text). In particular, we show that token repetition in the training data sequence plays an important role in ICL, and that the difficulty of the training task affects the appearance of ICL. Based on these insights, we successfully implement the ICL capability in a few-shot learning environment on a visual dataset and an EEG classification task. Key to this, we systematically reveal the properties of LLMs that enable ICL to work effectively in autoregressive models and various modalities.
Takeaways, Limitations
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Takeaways:
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Suggests the possibility of extending LLM's ICL capabilities to various modalities (vision, EEG, etc.).
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A training data organization strategy is presented to improve ICL (utilizing token repetition).
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Emphasize the importance of training task difficulty.
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Improving ICL performance in a small-shot learning environment.
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Limitations:
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Further research is needed to determine whether the presented insights can be generalized to all types of LLMs and modalities.
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Further exploration of other factors affecting ICL other than token repetition is needed.
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Only experimental results for specific modalities and tasks are presented, which may limit generalization.