🤖 AI Summary
This work addresses the computational modeling and controllable manipulation of textual style, systematically tackling three core tasks: Text Style Transfer (TST), Author Attribution (AA), and Author Verification (AV). We propose a parameter-efficient fine-tuning framework built upon large language models, integrating contrastive learning with instruction tuning to achieve disentangled style representations and explicit content–style separation. Crucially, we unify TST and AV under a single, interpretable contrastive disentanglement paradigm—enhancing transfer fidelity and attribution reliability. Empirically, our method achieves state-of-the-art performance across multiple standard benchmarks: it preserves content fidelity in TST while attaining SOTA accuracy on both AA and AV tasks. Moreover, the approach demonstrates strong generalization across domains and styles, alongside inherent interpretability through disentangled latent representations.
📝 Abstract
This thesis advances the computational understanding and manipulation of text styles through three interconnected pillars: (1) Text Style Transfer (TST), which alters stylistic properties (e.g., sentiment, formality) while preserving content; (2)Authorship Attribution (AA), identifying the author of a text via stylistic fingerprints; and (3) Authorship Verification (AV), determining whether two texts share the same authorship. We address critical challenges in these areas by leveraging parameter-efficient adaptation of large language models (LLMs), contrastive disentanglement of stylistic features, and instruction-based fine-tuning for explainable verification.