# Plug-and-Blend: A Framework for Controllable Story Generation with Blended Control Codes

arXiv: https://arxiv.org/abs/2104.04039 · HTML: https://mlsanity.com/paper/2104.04039/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 23 references that are in the map.
- First posted on arXiv 23 Mar 2021.

## Builds on (most cited first, 17 of 17)

- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [Plug and Play Language Models: A Simple Approach to Controlled Text Generation](https://mlsanity.com/paper/1912.02164/) (2019)
- [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://mlsanity.com/paper/1909.05858/) (2019)
- [Universal Adversarial Triggers for Attacking and Analyzing NLP](https://mlsanity.com/paper/1908.07125/) (2019)
- [GeDi: Generative Discriminator Guided Sequence Generation](https://mlsanity.com/paper/2009.06367/) (2020)
- [Toward Controlled Generation of Text](https://mlsanity.com/paper/1703.00955/) (2017)
- [Plan-And-Write: Towards Better Automatic Storytelling](https://mlsanity.com/paper/1811.05701/) (2018)
- [PlotMachines: Outline-Conditioned Generation with Dynamic Plot State Tracking](https://mlsanity.com/paper/2004.14967/) (2020)
- [UNION: An Unreferenced Metric for Evaluating Open-ended Story Generation](https://mlsanity.com/paper/2009.07602/) (2020)
- [STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generation](https://mlsanity.com/paper/2010.01717/) (2020)
- [Transformer-based Conditional Variational Autoencoder for Controllable Story Generation](https://mlsanity.com/paper/2101.00828/) (2021)
- [Plug-and-Play Conversational Models](https://mlsanity.com/paper/2010.04344/) (2020)
- [Pre-train and Plug-in: Flexible Conditional Text Generation with Variational Auto-Encoders](https://mlsanity.com/paper/1911.03882/) (2019)
- [Plug and Play Autoencoders for Conditional Text Generation](https://mlsanity.com/paper/2010.02983/) (2020)
- [Narrative Interpolation for Generating and Understanding Stories](https://mlsanity.com/paper/2008.07466/) (2020)
- [Co-Creative Level Design via Machine Learning](https://mlsanity.com/paper/1809.09420/) (2018)
- [Directed Beam Search: Plug-and-Play Lexically Constrained Language Generation](https://mlsanity.com/paper/2012.15416/) (2020)

## Built on by (most cited first, 19 of 19)

- [A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models](https://mlsanity.com/paper/2201.05337/) (2022)
- [Controllable Text Generation with Neurally-Decomposed Oracle](https://mlsanity.com/paper/2205.14219/) (2022)
- [DisCup: Discriminator Cooperative Unlikelihood Prompt-tuning for Controllable Text Generation](https://mlsanity.com/paper/2210.09551/) (2022)
- [Creating Suspenseful Stories: Iterative Planning with Large Language Models](https://mlsanity.com/paper/2402.17119/) (2024)
- [Beyond Prompts: Exploring the Design Space of Mixed-Initiative Co-Creativity Systems](https://mlsanity.com/paper/2305.07465/) (2023)
- [Reinforcement Learning with Token-level Feedback for Controllable Text Generation](https://mlsanity.com/paper/2403.11558/) (2024)
- [Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning](https://mlsanity.com/paper/2210.07792/) (2022)
- [LongStory: Coherent, Complete and Length Controlled Long story Generation](https://mlsanity.com/paper/2311.15208/) (2023)
- [Air-Decoding: Attribute Distribution Reconstruction for Decoding-Time Controllable Text Generation](https://mlsanity.com/paper/2310.14892/) (2023)
- [Creative Wand: A System to Study Effects of Communications in Co-Creative Settings](https://mlsanity.com/paper/2208.02886/) (2022)
- [SWAG: Storytelling With Action Guidance](https://mlsanity.com/paper/2402.03483/) (2024)
- [Story Shaping: Teaching Agents Human-like Behavior with Stories](https://mlsanity.com/paper/2301.10107/) (2023)
- [End-to-end Story Plot Generator](https://mlsanity.com/paper/2310.08796/) (2023)
- [Text-Only Training for Visual Storytelling](https://mlsanity.com/paper/2308.08881/) (2023)
- [Controllable Text Generation with Residual Memory Transformer](https://mlsanity.com/paper/2309.16231/) (2023)
- [PANGeA: Procedural Artificial Narrative using Generative AI for Turn-Based Video Games](https://mlsanity.com/paper/2404.19721/) (2024)
- [Semantic Space Grounded Weighted Decoding for Multi-Attribute Controllable Dialogue Generation](https://mlsanity.com/paper/2305.02820/) (2023)
- [DiNADO: Norm-Disentangled Neurally-Decomposed Oracles for Controlling Language Models](https://mlsanity.com/paper/2306.11825/) (2023)
- [XFBoost: Improving Text Generation with Controllable Decoders](https://mlsanity.com/paper/2202.08124/) (2022)

## Abstract (arXiv)

Large pre-trained neural language models (LM) have very powerful text generation capabilities. However, in practice, they are hard to control for creative purposes. We describe a Plug-and-Play controllable language generation framework, Plug-and-Blend, that allows a human user to input multiple control codes (topics). In the context of automated story generation, this allows a human user loose or fine-grained control of the topics and transitions between them that will appear in the generated story, and can even allow for overlapping, blended topics. Automated evaluations show our framework, working with different generative LMs, controls the generation towards given continuous-weighted control codes while keeping the generated sentences fluent, demonstrating strong blending capability. A human participant evaluation shows that the generated stories are observably transitioning between two topics.