类ChatGPT人工智能中温度驱动的反转与非线性动力学
Temperature-driven inversion and nonlinear dynamics in ChatGPT-like AIs
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中文总结 AI 辅助
该研究发现类ChatGPT AI的温度效应与普通多态系统相反，其经12000个续文实验揭示出群体反转等非线性动力学特征，还识别出隐藏坐标可预测输出重复，证明其是可控的新型非线性物理系统。
中文摘要 AI 辅助
对于普通多态系统，提高温度会增加对更多状态的访问，从而增大其熵。但我们发现类ChatGPT人工智能却相反，尽管提高解码器温度同样会增加对更多状态（下一个词选择）的访问。在对11个AI生成的12000个续文样本中，自回归反馈驱动长期输出群体达到熵最大值后进入群体反转，该转变具有冻结状态、周期、间歇性和噪声诱导有序性的特征。我们提供了隐藏坐标的证据，其作为有效非线性映射的状态变量，轨迹平均值可强烈预测独立测试轨迹中的输出重复。因此类ChatGPT人工智能并非“随机鹦鹉”，而是一类可测量和扰动内部动力学的新型可控非线性物理系统。
英文摘要
Increasing the temperature of an ordinary many-state system increases access to a wider range of states and hence increases its entropy. We find the opposite in ChatGPT-like AIs, even though raising the decoder temperature likewise increases access to a wider range of states (next-token choices). Across 12,000 continuations from 11 AIs, autoregressive feedback drives the long-time output population through an entropy maximum and into population inversion. The transition features frozen states, cycles, intermittency and noise-induced ordering. We present evidence of a hidden coordinate that acts as the state variable of an effective nonlinear map. Its trajectory average strongly predicts output repetition in separate test trajectories. ChatGPT-like AIs therefore behave not as `stochastic parrots', but as a new class of controllable nonlinear physical systems whose internal dynamics can be measured and perturbed.
发表机构
- The George Washington University（乔治·华盛顿大学）
机构由 AI 辅助整理，请以论文原文为准。
