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About this paper
Angel J. Gallego, Roman Orus
This paper establishes a foundational analogy between Chomsky's linguistic MERGE operation and the physical concept of information renormalization, representing probabilistic language models as Tensor Networks (TNs). This framework explains the observed long-range correlations in language as a consequence of hierarchical syntax and demonstrates high computational efficiency for language processing, while also showing how these models can be formulated as quantum states with derivable perplexity bounds.
Key insights
- Human language exhibits high computational efficiency (O(n) for probability calculation) due to the diagonal nature of MERGE tensors, supporting the idea of language as an optimally designed computational system.
- The hierarchical (tree-like) syntactic structure of language, when viewed through the Tensor Network lens, intrinsically leads to the observed polynomial decay of long-range correlations.
- Aligning AI language models with this inherent renormalization structure (e.g., using architectures akin to TTNs) can explain their effectiveness and guide future design.
Summary by alphaXiv.
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