Using Laplace Transform To Optimize the Hallucination of Generation Models
This work addresses the hallucination issue in generation models, which is a critical problem for improving reliability in AI applications, but it appears incremental as it applies existing control theory methods to this domain.
The authors tackled the problem of confident errors (hallucinations) in generation models by formalizing them as stochastic dynamical systems using control theory, and they fundamentally optimized the hallucination problem through Laplace transform analysis.
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.