arXiv:2606.06238v1 Announce Type: new Abstract: We propose a statistical-field framework for text generated by large language models (LLMs), treating token embeddings as continuous spin variables on a one-dimensional chain. Defining a susceptibility from the connected two-point correlator and an order parameter from the ensemble-averaged embedding field, we vary the \texttt{softmax} temperature $T$ and observe a sharp susceptibility peak near a characteristic $T_c$ with power-law-like scaling, a concurrent rapid change in the order parameter, and a collapse onto a single semantic direction bel
This research provides a framework emerging from concurrent advancements in statistical physics and AI, allowing for deeper theoretical understanding of LLM behaviors, particularly as model complexity increases.
Understanding the 'criticality' of LLM temperature scaling could enable more predictable and controllable large language models, impacting their reliability and safety in various applications.
The ability to define 'susceptibility' and 'order parameters' for LLMs suggests a pathway towards a more scientific, rather than empirical, approach to their design and fine-tuning, moving from art to engineering.
- · AI research institutions
- · LLM developers
- · Physics-informed AI startups
- · Empirical-only LLM development approaches
- · Companies unable to integrate advanced theoretical insights
This theoretical framework could lead to breakthroughs in LLM architecture design and parameter optimization.
Improved understanding of LLM 'criticality' might enable more robust and less 'hallucinatory' AI agents, accelerating their deployment in sensitive applications.
The development of 'semantic directions' and order parameters could allow for highly granular and controllable content generation, influencing information environments and creative industries.
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Read at arXiv cs.LG