# Controlling Pre-trained Language Models for Grade-Specific Text Simplification

arXiv: https://arxiv.org/abs/2305.14993 · HTML: https://mlsanity.com/paper/2305.14993/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 44 references that are in the map.
- First posted on arXiv 24 May 2023.

## Builds on (most cited first, 19 of 19)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Diffusion-LM Improves Controllable Text Generation](https://mlsanity.com/paper/2205.14217/) (2022)
- [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)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search](https://mlsanity.com/paper/1704.07138/) (2017)
- [Learning to Write with Cooperative Discriminators](https://mlsanity.com/paper/1805.06087/) (2018)
- [Sentence Simplification with Deep Reinforcement Learning](https://mlsanity.com/paper/1703.10931/) (2017)
- [Controllable Sentence Simplification](https://mlsanity.com/paper/1910.02677/) (2019)
- [Controlled Text Generation as Continuous Optimization with Multiple Constraints](https://mlsanity.com/paper/2108.01850/) (2021)
- [Exploring Controllable Text Generation Techniques](https://mlsanity.com/paper/2005.01822/) (2020)
- [Controllable Text Simplification with Explicit Paraphrasing](https://mlsanity.com/paper/2010.11004/) (2020)
- [Controlling Text Complexity in Neural Machine Translation](https://mlsanity.com/paper/1911.00835/) (2019)
- [Multi-Task Neural Models for Translating Between Styles Within and Across Languages](https://mlsanity.com/paper/1806.04357/) (2018)
- [An Imitation Learning Curriculum for Text Editing with Non-Autoregressive Models](https://mlsanity.com/paper/2203.09486/) (2022)
- [Explainable Prediction of Text Complexity: The Missing Preliminaries for Text Simplification](https://mlsanity.com/paper/2007.15823/) (2020)
- [Predicting the Relative Difficulty of Single Sentences With and Without Surrounding Context](https://mlsanity.com/paper/1606.08425/) (2016)
- [Controllable Sentence Simplification: Employing Syntactic and Lexical Constraints](https://mlsanity.com/paper/1910.04387/) (2019)
- [(Psycho-)Linguistic Features Meet Transformer Models for Improved Explainable and Controllable Text Simplification](https://mlsanity.com/paper/2212.09848/) (2022)

## Built on by (most cited first, 14 of 14)

- [From Tarzan to Tolkien: Controlling the Language Proficiency Level of LLMs for Content Generation](https://mlsanity.com/paper/2406.03030/) (2024)
- [Analysing Zero-Shot Readability-Controlled Sentence Simplification](https://mlsanity.com/paper/2409.20246/) (2024)
- [Standardize: Aligning Language Models with Expert-Defined Standards for Content Generation](https://mlsanity.com/paper/2402.12593/) (2024)
- [Exploiting Dialect Identification in Automatic Dialectal Text Normalization](https://mlsanity.com/paper/2407.03020/) (2024)
- [Simplifying Translations for Children: Iterative Simplification Considering Age of Acquisition with LLMs](https://mlsanity.com/paper/2408.04217/) (2024)
- [Evaluating LLMs for Targeted Concept Simplification for Domain-Specific Texts](https://mlsanity.com/paper/2410.20763/) (2024)
- [Text Simplification of Scientific Texts for Non-Expert Readers](https://mlsanity.com/paper/2307.03569/) (2023)
- [Aligning Sentence Simplification with ESL Learner's Proficiency for Language Acquisition](https://mlsanity.com/paper/2502.11457/) (2025)
- [Fine-grained Controllable Text Generation through In-context Learning with Feedback](https://mlsanity.com/paper/2406.11338/) (2024)
- [Rethinking the Role of Text Complexity in Language Model Pretraining](https://mlsanity.com/paper/2509.16551/) (2025)
- [Let's Simplify Step by Step: Guiding LLM Towards Multilingual Unsupervised Proficiency-Controlled Sentence Simplification](https://mlsanity.com/paper/2602.07499/) (2026)
- [Text Adaptation to Plain Language and Easy Read via Automatic Post-Editing Cycles](https://mlsanity.com/paper/2509.11991/) (2025)
- [Translation as Augmentation: Effect of Translated Data on Assessment of Difficulty](https://mlsanity.com/paper/2607.19101/) (2026)
- [SimpCue: Cue-Based Prompting for Multilingual Text Simplification](https://mlsanity.com/paper/2608.28042/) (2026)

## Abstract (arXiv)

Text simplification (TS) systems rewrite text to make it more readable while preserving its content. However, what makes a text easy to read depends on the intended readers. Recent work has shown that pre-trained language models can simplify text using a wealth of techniques to control output simplicity, ranging from specifying only the desired reading grade level, to directly specifying low-level edit operations. Yet it remains unclear how to set these control parameters in practice. Existing approaches set them at the corpus level, disregarding the complexity of individual inputs and considering only one level of output complexity. In this work, we conduct an empirical study to understand how different control mechanisms impact the adequacy and simplicity of text simplification systems. Based on these insights, we introduce a simple method that predicts the edit operations required for simplifying a text for a specific grade level on an instance-per-instance basis. This approach improves the quality of the simplified outputs over corpus-level search-based heuristics.