HULAT - UC 3 M at TSAR 2025 Shared Task A Prompt-Based Approach using Lightweight Language Models for Readability-Controlled Text Simplification (2025)

Abstract: This paper describes the participation of the HULAT-UC3M team in the TSAR 2025 Shared Task on Readability-Controlled Text Simplification. Our approach uses open and lightweight Large Language Models (LLMs) with different sizes, together with two strategies for prompt engineering. The proposed system has been tested on the trial data provided, and evaluated using the official metrics CEFR Compliance, Meaning Preservation, and Similarity to References. LLaMA 3 8B model with reinforced prompts was selected as our final proposal for submission, and ranking fourteenth according to the overall metric. Finally, we discuss the main challenges that we identified in developing our approach for this task.

## introduction automatic text simplification aims to generate texts that are easier to read and understand while preserving their original meaning @xcite . large language models (llms) are increasingly being explored for this purpose adapting generated content to match users' specific language proficiency levels. the common european framework of reference for languages (cefr) grades language mastery on a six-level scale, ranging from basic user (a1, a2), through independent user (b1, b2), to proficient user (c1, c2). despite the promise of llms for generating simplified texts, accurately classifying these outputs into specific cefr levels remains a significant challenge. current works indicate that llms often produce unsatisfactory results in cefr-related educational tasks, frequently overpredicting intermediate levels like b1 or b2 and struggling to consistently encode or apply intrinsic cefr knowledge without explicit prompting @xcite . this highlights the need for improved automatic evaluation metrics specifically tailored for readability-controlled text sim-plification (rcts), as standard metrics often misinterpret simplification operations and inaccurately assess readability and meaning preservation. hulat-uc3m participation in tsar 2025 shared task @xcite integrates generative llms through prompt engineering by testing open lightweight llms of different sizes using prompts with different levels of detail. ## related work the challenge of readability-controlled text simplification involves rewriting text to match specific audience proficiency levels, most commonly guided by the cefr. recent research has evidenced a concentrated effort to leverage the power of llms to achieve this fine-grained control @xcite . for instance, studies have investigated the performance of pre-trained instruction-tuned llms in aligning their generated output with standardized readability criteria, finding that models vary widely in their inherent ability accurately to encode and apply cefr requirements when performing generative tasks @xcite . in addition, methods have been developed to integrate readability control directly into the generation process itself, including techniques that use reinforcement learning and decoding approaches with anticipation mechanisms to minimize the gap between the requested and observed readability levels @xcite . a key methodological advancement in rcts has been the transition towards controlling complexity via prompt-based strategies. many recent approaches focus on the zero-shot or few-shot prompting, where pre-trained models are guided entirely by explicit or a small number of in-context examples to a target complexity level @xcite . these strategies allow modifying any source text to an absolute target read-ability level, often making the generated output less dependent on the complexity of the original source. the effectiveness of these techniques highlights the role of prompt richness, the degree of descriptive guidance provided, in controlling readability. this aspect is directly connected to our approach, which compares reinforced and slightly reinforced prompts as two levels of guidance for readability-controlled simplification. we conclude that, given the strong performance observed in recent literature, our approach will focus exclusively on zero-shot strategies for pretrained llms, leveraging only advanced prompt engineering to control text simplification. this methodology avoids the need for exhaustive finetuning on domain-specific corpora, allowing a highly flexible and efficient solution. ## proposed architecture two families of llms have been considered: ettin suite @xcite and meta llama 3 @xcite . as for prompt engineering, the reinforced prompts have consisted of the description of each cefr level in order to achieve the simplification of the original text to the required target cefr level. figure 1 shows the architecture of our approach. after reviewing the latest advances on recent, open, lightweight, and tasksuitable llms, llama 3 and ettin decoder are selected as generation modules. the prompt engineering techniques are developed for being applied with the adapted and cefr-aligned datasets. this key step involves the use of reinforced prompt and slightly reinforced prompt to guide the models' text generation. afterwards, the approach is assessed in the automatic evaluation metrics module, evaluated using the official set of evaluation metrics and our complementary ones. a manual review is also carried out in the form of a qualitative inspection. finally, after quantitative and qualitative analysis, a decision is made to select the final configuration for presentation. the final configuration was selected prioritizing cefr compliance and align-score, supported by manual qualitative inspection.