# Attribute Alignment: Controlling Text Generation from Pre-trained Language Models

arXiv: https://arxiv.org/abs/2103.11070 · HTML: https://mlsanity.com/paper/2103.11070/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 43 references that are in the map.
- First posted on arXiv 20 Mar 2021.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Fine-Tuning Language Models from Human Preferences](https://mlsanity.com/paper/1909.08593/) (2019)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [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)
- [Generating Sentences from a Continuous Space](https://mlsanity.com/paper/1511.06349/) (2015)
- [Wizard of Wikipedia: Knowledge-Powered Conversational agents](https://mlsanity.com/paper/1811.01241/) (2018)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [GeDi: Generative Discriminator Guided Sequence Generation](https://mlsanity.com/paper/2009.06367/) (2020)
- [Simple, Scalable Adaptation for Neural Machine Translation](https://mlsanity.com/paper/1909.08478/) (2019)
- [Controlling Linguistic Style Aspects in Neural Language Generation](https://mlsanity.com/paper/1707.02633/) (2017)
- [Learning to Generate Reviews and Discovering Sentiment](https://mlsanity.com/paper/1704.01444/) (2017)
- [Style Transfer in Text: Exploration and Evaluation](https://mlsanity.com/paper/1711.06861/) (2017)
- [Learning to Write with Cooperative Discriminators](https://mlsanity.com/paper/1805.06087/) (2018)
- [Exploring Controllable Text Generation Techniques](https://mlsanity.com/paper/2005.01822/) (2020)
- [CoCon: A Self-Supervised Approach for Controlled Text Generation](https://mlsanity.com/paper/2006.03535/) (2020)
- [Topic-Guided Variational Autoencoders for Text Generation](https://mlsanity.com/paper/1903.07137/) (2019)
- [Controlling Style in Generated Dialogue](https://mlsanity.com/paper/2009.10855/) (2020)
- [Plug-and-Play Conversational Models](https://mlsanity.com/paper/2010.04344/) (2020)
- [Encoder-Agnostic Adaptation for Conditional Language Generation](https://mlsanity.com/paper/1908.06938/) (2019)
- [Learning Structured Text Representations](https://mlsanity.com/paper/1705.09207/) (2017)

## Built on by (most cited first, 27 of 27)

- [Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias](https://mlsanity.com/paper/2306.15895/) (2023)
- [Controllable Natural Language Generation with Contrastive Prefixes](https://mlsanity.com/paper/2202.13257/) (2022)
- [A Distributional Lens for Multi-Aspect Controllable Text Generation](https://mlsanity.com/paper/2210.02889/) (2022)
- [Controllable Mixed-Initiative Dialogue Generation through Prompting](https://mlsanity.com/paper/2305.04147/) (2023)
- [DisCup: Discriminator Cooperative Unlikelihood Prompt-tuning for Controllable Text Generation](https://mlsanity.com/paper/2210.09551/) (2022)
- [Controllable Text Generation via Probability Density Estimation in the Latent Space](https://mlsanity.com/paper/2212.08307/) (2022)
- [Control Prefixes for Parameter-Efficient Text Generation](https://mlsanity.com/paper/2110.08329/) (2021)
- [Mixture of Soft Prompts for Controllable Data Generation](https://mlsanity.com/paper/2303.01580/) (2023)
- [PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learning](https://mlsanity.com/paper/2211.01761/) (2022)
- [Personalized Text Generation with Fine-Grained Linguistic Control](https://mlsanity.com/paper/2402.04914/) (2024)
- [Dynamic Multi-Reward Weighting for Multi-Style Controllable Generation](https://mlsanity.com/paper/2402.14146/) (2024)
- [MacLaSa: Multi-Aspect Controllable Text Generation via Efficient Sampling from Compact Latent Space](https://mlsanity.com/paper/2305.12785/) (2023)
- [Air-Decoding: Attribute Distribution Reconstruction for Decoding-Time Controllable Text Generation](https://mlsanity.com/paper/2310.14892/) (2023)
- [Boosting Reward Model with Preference-Conditional Multi-Aspect Synthetic Data Generation](https://mlsanity.com/paper/2407.16008/) (2024)
- [Effective Controllable Bias Mitigation for Classification and Retrieval using Gate Adapters](https://mlsanity.com/paper/2401.16457/) (2024)
- [Automatically Exposing Problems with Neural Dialog Models](https://mlsanity.com/paper/2109.06950/) (2021)
- [Attributes as Textual Genes: Leveraging LLMs as Genetic Algorithm Simulators for Conditional Synthetic Data Generation](https://mlsanity.com/paper/2509.02040/) (2025)
- [PCFG-based Natural Language Interface Improves Generalization for Controlled Text Generation](https://mlsanity.com/paper/2210.07431/) (2022)
- [Few-Shot Table-to-Text Generation with Prefix-Controlled Generator](https://mlsanity.com/paper/2208.10709/) (2022)
- [Controllable Text Generation with Residual Memory Transformer](https://mlsanity.com/paper/2309.16231/) (2023)
- [LiFi: Lightweight Controlled Text Generation with Fine-Grained Control Codes](https://mlsanity.com/paper/2402.06930/) (2024)
- [Multi-Aspect Controllable Text Generation with Disentangled Counterfactual Augmentation](https://mlsanity.com/paper/2405.19958/) (2024)
- [Bridging the Gap Between Training and Inference of Bayesian Controllable Language Models](https://mlsanity.com/paper/2206.05519/) (2022)
- [Funny or Persuasive, but Not Both: Evaluating Fine-Grained Multi-Concept Control in LLMs](https://mlsanity.com/paper/2601.18483/) (2026)
- [Design Your Ad: Personalized Advertising Image and Text Generation with Unified Autoregressive Models](https://mlsanity.com/paper/2605.12138/) (2026)
- [Reproducing the Metric-Based Evaluation of a Set of Controllable Text Generation Techniques](https://mlsanity.com/paper/2405.07875/) (2024)
- [HiCaM: A Hierarchical-Causal Modification Framework for Long-Form Text Modification](https://mlsanity.com/paper/2505.24319/) (2025)

## Abstract (arXiv)

Large language models benefit from training with a large amount of unlabeled text, which gives them increasingly fluent and diverse generation capabilities. However, using these models for text generation that takes into account target attributes, such as sentiment polarity or specific topics, remains a challenge. We propose a simple and flexible method for controlling text generation by aligning disentangled attribute representations. In contrast to recent efforts on training a discriminator to perturb the token level distribution for an attribute, we use the same data to learn an alignment function to guide the pre-trained, non-controlled language model to generate texts with the target attribute without changing the original language model parameters. We evaluate our method on sentiment- and topic-controlled generation, and show large performance gains over previous methods while retaining fluency and diversity.