Harnessing Stylistic Influence in NLP: A Gateway to Deeper Language Understanding

Abstract

The concept of style permeates all aspects of human interaction, shaping how we interpret, express, and engage with the world. Stylistic uniqueness is what distinguishes individuals and influences how they solve problems. In the context of language, style not only reflects personal idiosyncrasies but also plays a pivotal role in how meaning is conveyed and understood. This power of stylistic uniqueness is evident in its diverse applications. In digital forensics, identifying stylistic fingerprints can be lifesaving, helping trace authorship in critical situations. In education, it plays a key role in preventing plagiarism, ensuring originality, and enforcing copyright in scholarly work. These applications demonstrate the powerful role stylistic analysis can play in maintaining ethical standards and security in digital domains.

Beyond its original applications, the growing role of stylistic influence in NLP has reshaped many content-based tasks into profile-based approaches. For example, hate speech detection is no longer just about labeling content but now focuses on evaluating an author's propensity to spread harmful narratives. Methodologically, the field has progressed beyond classical and statistical models toward more advanced approaches. In our prior work, we explored the integration of stylistic signals with pretrained language models using novel architectures, including graph-based methods designed to better capture the structural and relational properties of style.

Despite these advances, two core challenges remain largely overlooked. First, many existing models lack support for class-incremental learning—an essential capability for adapting to newly emerging authors without full retraining. Second, they often fail to capture subtle yet distinctive stylistic cues that separate closely related authors. In this proposal, we address both challenges through a series of recent studies. One line of work introduces the problem of class-incremental learning in stylistic modeling. Building on that, we propose a new framework that combines metric learning with stylistic-semantic representation to enable continual learning while enhancing fine-grained author discrimination. This approach outperforms state-of-the-art methods across several authorship attribution benchmarks.

Finally, as large language models (LLMs) become more prevalent, it is crucial for these models to develop their own stylistic distinctions to safeguard intellectual property rights and prevent misattribution. To this end, we explore Fermi problems—a unique reasoning-based benchmark—to investigate how LLMs can cultivate distinctive stylistic traits in their problem-solving capabilities.