# Personalized Text Generation with Fine-Grained Linguistic Control

arXiv: https://arxiv.org/abs/2402.04914 · HTML: https://mlsanity.com/paper/2402.04914/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 54 references that are in the map.
- First posted on arXiv 7 Feb 2024.

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

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling](https://mlsanity.com/paper/2304.01373/) (2023)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [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)
- [LaMP: When Large Language Models Meet Personalization](https://mlsanity.com/paper/2304.11406/) (2023)
- [GeDi: Generative Discriminator Guided Sequence Generation](https://mlsanity.com/paper/2009.06367/) (2020)
- [Training Millions of Personalized Dialogue Agents](https://mlsanity.com/paper/1809.01984/) (2018)
- [A Confederacy of Models: a Comprehensive Evaluation of LLMs on Creative Writing](https://mlsanity.com/paper/2310.08433/) (2023)
- [Personalizing Dialogue Agents via Meta-Learning](https://mlsanity.com/paper/1905.10033/) (2019)
- [Controllable Natural Language Generation with Contrastive Prefixes](https://mlsanity.com/paper/2202.13257/) (2022)
- [CoEdIT: Text Editing by Task-Specific Instruction Tuning](https://mlsanity.com/paper/2305.09857/) (2023)
- [Personalized Machine Translation: Preserving Original Author Traits](https://mlsanity.com/paper/1610.05461/) (2016)
- [Content preserving text generation with attribute controls](https://mlsanity.com/paper/1811.01135/) (2018)
- [Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generation](https://mlsanity.com/paper/2204.08128/) (2022)
- [One Chatbot Per Person: Creating Personalized Chatbots based on Implicit User Profiles](https://mlsanity.com/paper/2108.09355/) (2021)
- [UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis](https://mlsanity.com/paper/2110.00135/) (2021)
- [Attribute Alignment: Controlling Text Generation from Pre-trained Language Models](https://mlsanity.com/paper/2103.11070/) (2021)
- [Mixture of Soft Prompts for Controllable Data Generation](https://mlsanity.com/paper/2303.01580/) (2023)
- [Compositional Demographic Word Embeddings](https://mlsanity.com/paper/2010.02986/) (2020)
- [Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text Generation](https://mlsanity.com/paper/2004.14983/) (2020)
- [Exploring the Value of Personalized Word Embeddings](https://mlsanity.com/paper/2011.06057/) (2020)
- [Personalizing Grammatical Error Correction: Adaptation to Proficiency Level and L1](https://mlsanity.com/paper/2006.02964/) (2020)
- [LMSOC: An Approach for Socially Sensitive Pretraining](https://mlsanity.com/paper/2110.10319/) (2021)
- [Fast Rhetorical Structure Theory Discourse Parsing](https://mlsanity.com/paper/1505.02425/) (2015)
- [PerPLM: Personalized Fine-tuning of Pretrained Language Models via Writer-specific Intermediate Learning and Prompts](https://mlsanity.com/paper/2309.07727/) (2023)
- [Toward Word Embedding for Personalized Information Retrieval](https://mlsanity.com/paper/1606.06991/) (2016)
- [Examination and Extension of Strategies for Improving Personalized Language Modeling via Interpolation](https://mlsanity.com/paper/2006.05469/) (2020)

## Built on by (most cited first, 20 of 20)

- [LongLaMP: A Benchmark for Personalized Long-form Text Generation](https://mlsanity.com/paper/2407.11016/) (2024)
- [Personalized Text Generation with Contrastive Activation Steering](https://mlsanity.com/paper/2503.05213/) (2025)
- [SynthesizeMe! Inducing Persona-Guided Prompts for Personalized Reward Models in LLMs](https://mlsanity.com/paper/2506.05598/) (2025)
- [A Survey of Personalization: From RAG to Agent](https://mlsanity.com/paper/2504.10147/) (2025)
- [Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language Models](https://mlsanity.com/paper/2409.09510/) (2024)
- [Social Skill Training with Large Language Models](https://mlsanity.com/paper/2404.04204/) (2024)
- [Personalized Graph-Based Retrieval for Large Language Models](https://mlsanity.com/paper/2501.02157/) (2025)
- [Improve LLM-based Automatic Essay Scoring with Linguistic Features](https://mlsanity.com/paper/2502.09497/) (2025)
- [JAM: Controllable and Responsible Text Generation via Causal Reasoning and Latent Vector Manipulation](https://mlsanity.com/paper/2502.20684/) (2025)
- [Large Language Model-based Role-Playing for Personalized Medical Jargon Extraction](https://mlsanity.com/paper/2408.05555/) (2024)
- [Improving RAG for Personalization with Author Features and Contrastive Examples](https://mlsanity.com/paper/2504.08745/) (2025)
- [Evaluating Style-Personalized Text Generation: Challenges and Directions](https://mlsanity.com/paper/2508.06374/) (2025)
- [CAP-LLM: Context-Augmented Personalized Large Language Models for News Headline Generation](https://mlsanity.com/paper/2508.03935/) (2025)
- [Iterative Critique-Refine Framework for Enhancing LLM Personalization](https://mlsanity.com/paper/2510.24469/) (2025)
- [PeReGrINE: Evaluating Personalized Review Fidelity with User Item Graph Context](https://mlsanity.com/paper/2604.07788/) (2026)
- [PrefReward: Learning User Preference Matrix for Personalized Text Generation](https://mlsanity.com/paper/2607.21067/) (2026)
- [LingGen: Scalable Multi-Attribute Linguistic Control via Power-Law Masking](https://mlsanity.com/paper/2410.24201/) (2024)
- [Syntax-Guided Diffusion Language Models with User-Integrated Personalization](https://mlsanity.com/paper/2510.01028/) (2025)
- [Formalizing Style in Personal Narratives](https://mlsanity.com/paper/2510.08649/) (2025)
- [One Prompt Does Not Fit All: Self-Meta-Evolve for Personalized Information Extraction](https://mlsanity.com/paper/2609.21626/) (2026)

## Abstract (arXiv)

As the text generation capabilities of large language models become increasingly prominent, recent studies have focused on controlling particular aspects of the generated text to make it more personalized. However, most research on controllable text generation focuses on controlling the content or modeling specific high-level/coarse-grained attributes that reflect authors' writing styles, such as formality, domain, or sentiment. In this paper, we focus on controlling fine-grained attributes spanning multiple linguistic dimensions, such as lexical and syntactic attributes. We introduce a novel benchmark to train generative models and evaluate their ability to generate personalized text based on multiple fine-grained linguistic attributes. We systematically investigate the performance of various large language models on our benchmark and draw insights from the factors that impact their performance. We make our code, data, and pretrained models publicly available.