A Proposed Ehrenfeucht-Fraïssé Game Model for Natural Language Processing Generative Adversarial Networks

Abstract

Large Language Models (LLM’s) (e.g., ChatGPT) constitute both a significant research area and commercial application of AI. Current major LLM’s are built on Generative Pre-Trained Transformer (GPT) neural network architecture to perform natural language processing (NLP) tasks. Generative Adversarial Network (GAN) is another popular neural network architecture, which leverages a zero-sum game between constituent neural networks within the architecture to train the GAN, and is widely used for visual data applications. This article proposes a new GAN architecture for NLP: an EF-GAN whose underlying algorithm uses Ehrenfeucht–Fraïssé (EF) games, a game-theoretic approach from model theory to determine elementary equivalence of two structures, to perform NLP tasks. Li first discusses Abstract Meaning Representation (AMR) graphs of sentences, which constitute the structure(s) by which the EF-GAN algorithm iterates over. Then, through outlining EF games and considering how such EF games can be played on AMR graphs, Li illustrates an EF-GAN’s underlying algorithm. Having described the necessary mathematical framework, Li provides a description of an EF-GAN with an EF game played between a “Spoiler" neural network and a “Duplicator" neural network. While it is unclear whether the proposed EF-GAN model would perform similarly to current NLP models (e.g., GPT’s), Li argues that the proposed NLP model proposed may be a viable alternative generative model for NLP and discusses refinements for the proposed model and directions for future research.