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From Frege to chatGPT: Compositionality in language, cognition, and deep neural networks

Created by
  • Haebom

Author

Jacob Russin, Sam Whitman McGrath, Danielle J. Williams

Outline

This paper provides an overview of recent deep learning research on compositionality, a core property of human intelligence, for readers in philosophy, cognitive science, and neuroscience. Focusing on large-scale language models (LLMs), we discuss two approaches to achieving combinatorial generalization, which enables infinite expressive power from limited learning experience: (1) structural inductive bias and (2) meta-learning. We argue that the pre-training process of LLMs can be understood as a type of meta-learning, which allows deep neural networks (DNNs) to achieve combinatorial generalization. We then discuss the implications of these findings for the study of compositionality in human cognition and future research directions.

Takeaways, Limitations

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Takeaways:
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Large-scale language models (LLMs) have demonstrated combinatorial generalization capabilities, suggesting that neural networks could potentially achieve human-level cognitive abilities.
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We demonstrate that structural inductive bias and meta-learning are effective ways to impart combinatorial generalization capabilities to neural networks.
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It provides a new perspective on understanding the pre-learning process of LLM as meta-learning.
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It presents a new perspective and future research direction on the combinatorial nature of human cognition.
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Limitations:
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Further research is needed to determine whether LLM's combinatorial abilities are equivalent to those of humans.
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A deeper understanding of the precise mechanisms of structural inductive bias and meta-learning is needed.
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Further research is needed on the limits and constraints of the combinatorial capabilities of LLM.
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