Using Laplace Transform To Optimize the Hallucination of Generation Models

TL;DR

This paper models generation models as stochastic dynamical systems and uses Laplace transform analysis to understand and reduce hallucinations, offering a new control-theoretic approach to optimize their outputs.

Contribution

It introduces a control theory framework for analyzing GMs and proposes Laplace transform-based methods to mitigate hallucinations in generated outputs.

Findings

System response simulation helps address hallucinations.

Training progress aligns with system response.

Laplace transform analysis can optimize GMs to reduce hallucinations.

Abstract

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.