Generating Under the Influence: An Adversarial Approach to Modeling Stylistic Influences in Literary Text

Abstract

This study proposes, implements, and evaluates a method for simulating literary stylistic influence in a text generation system. Specifically, it probes the effects of enhancing next-token prediction training with the introduction of an additional objective, borrowed from the generative adversarial paradigm, for the purpose of administering stylistic pressures. Two crucial adaptations are applied to the typical adversarial objective. First, in order to nudge the model toward preferred styles, the objective is expanded from binary to multi-label. And second, in order to cushion the model against the inherent volatility of the adversarial training signal, losses related to non-preferred styles are zeroed out and ignored. All model components are warm-started from the historically pre-trained MacBERTh and fine-tuned on a bespoke corpus of 1950s Anglophone prose fiction. The study additionally devises evaluation metrics grounded in relevant critical and pedagogical literature. The implementation of this socially-adaptive text generation system not only demonstrates a viable approach to modeling peer stylistic influence but may moreover serve as a building block for future research on cultural evolution systems in the literary domain.