This repository is a curated collection of research papers on story generation, specifically focusing on advancements and applications within the Large Language Model (LLM) era. It serves as a valuable resource for researchers, developers, and enthusiasts interested in the latest techniques, datasets, and evaluation methods for AI-driven storytelling.

How It Works

The repository organizes papers chronologically (most recent first) and categorizes them by sub-topics such as "Plan And Write," "Multi Agent," "More Controllable," "More Personalized," and "Evaluation." This structure allows users to quickly navigate and discover relevant research in specific areas of story generation.

Quick Start & Requirements

This repository is a collection of links to papers and resources, not a runnable codebase. No installation or specific requirements are needed to browse the content.

Highlighted Details

• Comprehensive coverage of LLM-era story generation research, updated regularly.
• Categorization by key themes like planning, multi-agent collaboration, controllability, personalization, and evaluation.
• Links to papers, code (where available), and authors for each entry.
• Includes sections on relevant datasets and public resources for story generation tasks.

Maintenance & Community

The repository is actively maintained by Yingpeng Ma and Yan Ma, with contributions encouraged via issues or pull requests. Contact information is provided for inquiries.

Licensing & Compatibility

The repository itself is not licensed as it is a collection of links. Individual papers and code repositories linked within will have their own respective licenses.

Limitations & Caveats

This resource is a curated list and does not provide any executable code or direct tools for story generation. Users must follow the links to access the actual research papers and their associated codebases.