mlsanityData to 7 Oct 2026

Pastiche Novel Generation Creating: Fan Fiction You Love in Your Favorite Author's Style

arXiv 2502.15616 ↗First posted 21 Feb 2025cs.CL cs.AIcited by 1

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Abstract (arXiv)

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 Pastiche Novel Generation, which requires the generated novels to imitate the distinctive features of the original work, including understanding character profiles, 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 pastiche. 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 Harry Potter and 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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 50 of its 71 references that are in the map. First posted on arXiv 21 Feb 2025.

Builds on

50 of 71 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026LLMs Should Incorporate Explicit Mechanisms for Human Empathycited by 0