arXiv:2406.05335v3 Announce Type: replace-cross Abstract: Generation of text and speech in natural languages can be modeled as a stochastic process. This idea dates back to the seminal work of Markov and, later, to that of Shannon and also underlies the recent development of large language models (LLMs). The stochastic processes corresponding to natural languages should be distinct from those that generate nonlinguistic sequences. One of the features that discriminate linguistic and nonlinguistic sequences is power-law behavior, which is universally observed across different languages. In stat
This research, published on arXiv, indicates a deeper theoretical understanding of Large Language Models (LLMs) is emerging, linking them to fundamental principles of natural language and physics-like phase transitions.
Understanding the 'criticality' of natural languages and phase transitions in LLMs could lead to more robust, efficient, and capable AI, moving beyond empirical development to theoretically grounded design.
The theoretical framework for developing and analyzing LLMs could shift from purely empirical approaches to one informed by physical and statistical critical phenomena, potentially guiding future AI architecture and learning processes.
- · AI researchers
- · LLM developers
- · Computational linguists
- · Empirical-only AI development paradigms
Improved understanding of LLM limitations and capabilities based on theoretical underpinnings.
Development of novel LLM architectures designed from first principles of critical phenomena, leading to more human-like language generation.
These theoretically advanced LLMs could dramatically accelerate AI agent capabilities and reduce computational inefficiencies over current models.
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Read at arXiv cs.LG