R ead C trl: Personalizing text generation with readability-controlled instruction learning (2025)

Abstract: Content generation conditioning on users’ readability is an important application for personalization. In an era of large language models (LLMs), readability-controlled text generation based on LLMs has become increasingly important. This paper introduces a novel methodology called “Readability-Controlled Instruction Learning (ReadCtrl),” which aims to instruction-tune LLMs to tailor users’ readability levels. Unlike the traditional methods, which primarily focused on categorical readability adjustments—typically classified as high, medium, and low or expert and layperson levels—with limited success, ReadCtrl introduces a dynamic framework that enables LLMs to generate content at various (near continuous level) complexity levels, thereby enhancing their versatility across different applications. Our results show that the ReadCtrl-Mistral-7b models significantly outperformed strong baseline models such as GPT-4 and Claude-3, with a win rate of 52.1%:35.7% against GPT-4 in human evaluations. Furthermore, ReadCtrl has shown significant improvements in automatic evaluations, as evidenced by better readability metrics (e.g., FOG, FKGL) and generation quality metrics (e.g., BLEU, SARI, SummaC-Factuality, UniEval-Consistency and Coherence). These results underscore ReadCtrl’s effectiveness and tenacity in producing high-quality, contextually appropriate outputs that closely align with targeted readability levels, marking a significant advancement in personalized content generation using LLMs.

## introduction existing personalization methods mainly focus on the semantics of the content that individuals need, such as retrieving information based on individuals' search queries @xcite @xcite figure 1 : readctrl instruction following ability. while current sota llms such as gpt and claude (under the few-shot setting) show an upward trend in aligning their output with the target grade level, they fall significantly short of the 'perfect' adherence curve. other weaker llms like mistral-7b demonstrate little to no capacity to adjust to readctrl instructions, as indicated by the flat line parallel to the x-axis. notably, mistral-readctrl's performance closely approaches 'perfect', showcasing its advanced capability to tailor output to the specified readability level as set out by readctrl instructions. @xcite and summarization based on content representation @xcite . however, one important aspect of personalization that has not been widely explored is readabilitycontrolled content generation, which is particularly relevant for writers and readers with varying expertise levels @xcite . this involves tailoring content to match individuals' readability levels, which can vary widely due to differences in education, domain familiarity, and writing proficiency @xcite . the emergence of large language models (llms) has further advanced this field, ushering in a transformative era of personalized writing assistance @xcite . it is crucial for content generated by these models to be not only accurate and relevant but also adaptable to the stylistic and cognitive preferences of different users (e.g., writers and readers). the emphasis on customized content creation underscores the critical role of llm alignment methods in digital interactions, especially in environments where users may require varying levels of guidance and editorial control @xcite @xcite . at the heart of this innovative area are the principles of readability control instructions, designed to dynamically adapt the output vocabulary distribution to the specific context of each interaction. this can be achieved by analyzing chat history, interpreting user profiles, or responding to direct interaction requests, significantly enhancing llms' versatility @xcite . previous efforts in the domain of controllable text generation have primarily centered on binary readability adjustments, typically categorized into tasks of simplification, complication, or sibling style transfer @xcite @xcite . despite their objectives, these approaches often fail to fully address the diverse personalization needs due to the limited variety in training data and a concentrated emphasis on readability. in traditional supervised finetuning scenarios, designing multiple readabilitylevel ground truths for training data to facilitate readability control is not scalable. as a result, models may not have sufficient exposure to varied text complexities, limiting their ability to adjust content according to user-specific readability needs accurately. in response, the field of artificial intelligence is shifting towards more dynamic systems that can adapt outputs to meet users' unique preferences and requirements @xcite . this shift is heralding a new era of personalized content creation that extends beyond standard domains like information retrieval to specialized areas, enhancing user engagement and satisfaction through tailored content. this paper addresses these challenges by introducing a novel methodology termed "readabilitycontrolled instruction learning (readctrl)." our findings demonstrate that readctrl can empower llms to transform input text into content with controlled readability accurately. as illustrated in figure 1 , our investigation across a range of state-of-the-art llms shows varying degrees of compliance with readability-controlled instructions. mainstream models like gpt @xcite and claude @xcite , despite demonstrating an upward trend, fall significantly short of the ideal 'perfect' adherence curve-they show a tendency towards compliance but lack precise output control. in contrast, models that previously struggled with readability control, such as mistral-7b @xcite -illustrated almost as a horizontal line in the figure-have shown significant enhancement with the integration of readctrl, such as mistral-readctrl. these models now nearly meet the ideal performance curve, showcasing their improved ability to customize outputs to specific readability demands. specifically, readctrl incorporates explicit instruction tuning based on readability and is rigorously tested through tasks designed to evaluate the model's ability to adjust output complexity. these tasks include text simplification, which aims to reduce the output's readability relative to the input; paraphrase generation, which maintains the input's readability; and semantic entailment generation, which adjusts the output's readability, potentially increasing or decreasing it in relation to the input. by employing a clear instruction-"given an input text, please output an entailment with a readability score around {target readability score}"-models like mistral-readctrl demonstrate the efficacy of readctrl in generating content that is not only semantically coherent but also finely adjusted to meet diverse comprehension needs and preferences. in our initial experiments, we evaluated the model's performance in a "seen setting," where models were tested using datasets on which they were trained, such as asset @xcite ) for text simplification, paws @xcite for paraphrase generation, and snli @xcite for semantic entailment. this setting established a baseline for instruction tuning, confirming that the models could effectively adhere to readability instructions in familiar contexts. subsequent experiments involved an "unseen setting," testing the models against new datasets they had not encountered during training, such as wikismall @xcite ) for text simplification, mrpc @xcite for paraphrase generation, and multinli @xcite for semantic entailment. this phase was critical for assessing the models' adaptability and accuracy in novel contexts, reflecting their generalizability and real-world applicability. we utilized several readability assessment metrics, including the gunning fog index @xcite and @xcite , to quantitatively evaluate the complexity of the generated texts. additionally, we employed a range of automatic generation metrics for generation quality evaluation, such as bleu @xcite , sari @xcite , factuality @xcite , consistency and coherence @xcite , to assess the quality of the generated texts, aiming to balance readability, information retention, factuality, consistency, and coherence during evaluation. these evaluations confirmed the effectiveness of our readctrl methodology across a diverse range of tasks and datasets. particularly, mistral-readctrl excelled in both seen and unseen settings, showcasing robust performance metrics. for instance, in the unseen mrpc dataset, mistral readctrl achieved the lowest readability gap (1.66), the highest factuality (0.8184), and excellent bleu (0.3798) and sari (44.4327) scores, significantly outperforming competitors like gpt-4 and claude-3. in the wikismall dataset, it recorded a readability gap of just 2.09, the highest coherence score (0.9763), and a strong sari score of 42.1033. furthermore, detailed human and llmas-a-judge @xcite evaluations were conducted to compare mistral-readctrl with gpt-4 across different tasks and readability requirements. mistral-readctrl demonstrated superior performance, achieving a win rate of 52.1% in human evaluations and 58.3% in ai assessments, compared to gpt-4's 35.7% and 38.4%, respectively. notably strong results were observed in tasks involving wikismall (62.5% in human eval, 67.7% in ai eval) and snli (66.7% in human eval). ## related work early efforts for readability control in natural language generation (nlg) included microplanning steps to tailor the text to match different target reading levels @xcite @xcite . more recent studies, such as those by @xcite , have investigated controllable abstractive and extractive approaches for generating summaries tailored for layman and expert audiences from biomedical documents. concurrently, recent work @xcite ; @xcite ; @xcite examined the ability of the language models to adapt its outputs to different target audiences and writing styles, ranging from formal to informal, whereas imperial (2022) highlighted that gpt2 models struggle with preserving the linguistic complexity of input prompts. significant developments have also been made in models for plain language summarization (pls) from scientific papers @xcite @xcite , focusing on generating simplified texts that retain the original content's meaning. text simplification aims to enhance the readability of sentences by reducing their linguistic complexity, with various important societal applications, such as increasing accessibility for those with cognitive disabilities and also for patient education, non-native speakers, and children with reading difficulties @xcite . various aspects of simplified outputs have been addressed, including adapting to specific levels @xcite , incorporating edit operations @xcite , enforcing lexical and syntactic constraints @xcite , applying linguistically motivated syntactic rules @xcite , and integrating complex span extraction and lay language generation @xcite @xcite into the simplification process. in contrast to traditional text simplification, which primarily focuses on controlling the extent of paraphrasing, our approaches are designed to produce succinct and informative output for various tasks in both seen and unseen settings, while maintaining different fine-grained levels of desired readability. finally, our work follows the instruction tuning technique @xcite to help llms learn to follow readability-controlled instructions. traditional supervised fine-tuning (sft) techniques often struggle with fine-grained readability control, as they depend on manual annotation or synthetic data generation for enriching readability data, followed by model fine-tuning. this method requires considerable financial and time resources, with repeated tasks for each domain-specific application. alternatively, recent advances in instruction learning offer a more generalized approach, as highlighted in several studies @xcite @xcite @xcite . instruction learning operates on the premise that the model already possesses the necessary knowledge and skills to perform the target task but requires instructional data to learn how to follow the instructions effectively. by using a flan-style instruction fine-tuning method @xcite , we gathered taskspecific instructions for readctrl and conducted fine-tuning. our evaluations, both automatic and human, on seen and unseen tasks, confirm readctrl's effectiveness, simplifying the adaptation process and broadening application scope with minimal data needs. ## conclusion the readctrl approach enhances the adaptability of llms by dynamically adjusting content complex-ity to bridge the gap between writers and readers with diverse expertise. by outperforming mainstream models like gpt-4 in evaluations, mistral-readctrl demonstrates its capability to generate nuanced, high-quality outputs, highlighting its potential in enabling personalized writing and reading experiences.