From Style to Story: A Curriculum Learning Approach for Imitative Novel Generation

ACL ARR 2026 January Submission4952 Authors

Keywords: large language model, text generation

Abstract:

Great novels create immersive worlds with rich character arcs, well-structured plots, and nuanced writing styles.
However, current novel generation methods often rely on brief, simplistic story outlines and generate details using plain, generic language.
To bridge this gap, we introduce the task of $\textit{Imitative Novel Generation}$, which requires the generated novels to imitate the distinctive features of the original work, including understanding character profiles and world views, predicting plausible plot developments, and writing concrete details using vivid, expressive language.
To achieve this, we propose WriterAgent, a novel generation system designed to master the core aspects of literary imitative.
WriterAgent is trained through a curriculum learning paradigm, progressing from low-level stylistic mastery to high-level narrative coherence.
Its key tasks include language style learning, character modeling, plot planning, and stylish writing, ensuring comprehensive narrative control.
To support this, WriterAgent leverages the WriterLoRA framework, an extension of LoRA with hierarchical and cumulative task-specific modules, each specializing in a different narrative aspect.
We evaluate WriterAgent on multilingual classics like \textit{Harry Potter} and $\textit{Dream of the Red Chamber}$, demonstrating its superiority over baselines in capturing the target author’s settings, character dynamics, and writing style to produce coherent, faithful narratives.
We hope this work inspires literary creativity in NLP: ${WriteAgent}$.

Paper Type: Long

Research Area: AI/LLM Agents

Research Area Keywords: large language model, text generation

Contribution Types: NLP engineering experiment

Languages Studied: English, Chinese

Submission Number: 4952