用拉普拉斯变换分析生成模型幻觉，发现训练过程与系统响应一致。
Using Laplace Transform To Optimize the Hallucination of Generation Models
- 将生成模型视为随机动力系统，从控制论角度建模。
- 训练过程与系统响应高度一致，揭示优化路径。
- 为减少幻觉提供新理论视角，适合模型开发者参考。
为探索避免生成模型（GMs）产生自信错误（即幻觉）的可行性，本文通过控制理论视角，将生成模型系统形式化为一类随机动力系统。多种因素可能导致生成模型学习过程中的幻觉，利用控制理论知识可分析其系统功能与响应特性。由于生成模型在使用不同优化方法时具有高度复杂性，难以直接求解其拉普拉斯变换，但从宏观视角模拟源响应，为缓解生成模型幻觉提供了虚拟路径。研究还发现，训练进程与对应系统响应保持一致，为设计更优优化组件提供了有效途径。最终，通过拉普拉斯变换分析，从根本上优化了生成模型的幻觉问题。
原文摘要 · Abstract (English)
To explore the feasibility of avoiding the confident error (or hallucination) of generation models (GMs), we formalise the system of GMs as a class of stochastic dynamical systems through the lens of control theory. Numerous factors can be attributed to the hallucination of the learning process of GMs, utilising knowledge of control theory allows us to analyse their system functions and system responses. Due to the high complexity of GMs when using various optimization methods, we cannot figure out their solution of Laplace transform, but from a macroscopic perspective, simulating the source response provides a virtual way to address the hallucination of GMs. We also find that the training progress is consistent with the corresponding system response, which offers us a useful way to develop a better optimization component. Finally, the hallucination problem of GMs is fundamentally optimized by using Laplace transform analysis.
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