GRIPF at TSAR 2025 Shared Task Towards controlled CEFR level simplification with the help of inter-model interactions (2025)

Abstract: In this contribution to the CEFR level simplification TSAR 2025 Shared Task, we propose two systems, EZ-SCALAR and SAGA, that implement two differing approaches to prompting LLMs for proficiency-adapted simplification. Our results place us in the middle of the participating teams, and reveal that using external lexical resources to guide simplification improves overall results.

## introduction this paper presents the details of the gripf team in the tsar 2025 shared task on cefr level simplification @xcite . the workshop website describes the task as follows: "the task targets english-language paragraphs written at upper-intermediate or advanced levels and requires participants to simplify them according to a specified target readability level, defined using the common european framework of reference for languages (cefr). specifically, participants will be asked to simplify texts originally at b2 level or above to target levels of a1, a2, or b1" (tsar workshop). 1 ## related work as bahrainian et al. (2024) put it, "[t]ext simplification is the process of rewriting a piece of text using simpler vocabulary and grammatical structure in order to make the text more accessible and understandable for a larger audience". text simplification has evolved from rule-based approaches that rely on predefined linguistic transformations to neural methods that learn simplification patterns from data. early work focused on lexical substitution and syntactic restructuring using hand-crafted rules, while recent advances have leveraged transformer-based models for end-to-end simplification. @xcite demonstrate that lexically constrained decoding with edit operations can effectively guide neural simplification models, addressing the challenge of loose constraints in previous approaches. controllable text generation has emerged as a particularly relevant area, where models are guided to produce outputs meeting specific criteria such as reading level or target audience. @xcite propose document-level planning approaches that decompose simplification into discrete operations (copy, rephrase, split, or delete), showing that structured planning can guide generation more effectively than end-to-end approaches. recent work by @xcite addresses the challenge of evaluating document-level simplification, proposing methods to adapt sentence-level metrics for longer texts. within this landscape, cefr-level simplification represents a specialized challenge, as it requires not only reducing complexity but doing so in alignment with established language proficiency standards. @xcite highlight the particular challenges of multilingual simplification, while @xcite extend this work by developing parallel corpora for the simplification of medical texts in nine languages, demonstrating that multilingual training can improve the performance of simplification. ensemble methods in natural language processing have demonstrated consistent improvements over single-model approaches across various tasks. the principle of combining multiple models to leverage their complementary strengths has been successfully applied to machine translation, text generation, and classification tasks. recent work by @xcite shows that inprompt ensemble methods, where multiple model predictions are integrated as separate expert solutions with trust scores, can achieve strong per-formance in specialized domains. @xcite demonstrate that smaller ensemble models with refined outputs can consistently outperform individual models and even large-scale llms, suggesting that ensemble approaches may be more effective than relying solely on large standalone models. niess and kern (2025) extend ensemble methods to watermarking applications, showing that multi-feature ensemble approaches achieve 98% detection rates and maintain robustness against paraphrasing attacks. our work extends this concept to controllable simplification, where inter-model critique and refinement can potentially address the limitations of individual models. this approach aligns with recent trends in self-correction and iterative improvement in language models, where systems refine their output through multiple generation cycles. @xcite provide a comprehensive survey of automated correction strategies, categorizing approaches into training-time, generationtime, and post-hoc methods, while @xcite demonstrate that decompose-critique-refine pipelines can significantly improve constraint following in language models. however, recent critical surveys suggest that the effectiveness of selfcorrection varies significantly across tasks and model types @xcite , highlighting the importance of ensemble approaches that can leverage external critique mechanisms rather than relying solely on self-evaluation. ## methodology we use two similar approaches to tackle the problem of lexical simplification. the first system is called ez-scalar (ensemble zero-shot simplify, criticize, adapt with lexical assessment, and referee). the second system is called saga (self-assessed guided adaptation). both systems estimate complex vocabulary, either using external resources or by prompting the model. both models adjust the simplification based on the identified complex vocabulary. ## evaluation 4.1 pre-evaluation for pre-evaluating our systems, we used the provided trial data set and the official evaluation script. the evaluation script outputs different measures, organized into three overarching parts, each with sub-measures: ## discussion the superior performance of ez-scalar lex (avgscore: 0.070) over regular ez-scalar (0.060) demonstrates the value of explicit lexical guidance in controlled simplification tasks. this improvement appears primarily in the rmse metric (0.689 vs 0.721), suggesting that the lexical assessment helps achieve a more accurate cefr level targeting. the marginal difference in meaning preservation scores (m-bert-orig: 0.857 vs. 0.856) indicates that lexical guidance improves level compliance without significantly compromising semantic fidelity. saga's performance profile reveals interesting trade-offs in the system design space. although it achieves the highest bertscore-orig (0.951), indicating a strong preservation of surface-level similarity to the original text, it shows a weaker performance in cefr compliance (rmse: 0.831) and reference similarity. this suggests that the iterative proposer-reviewer approach may prioritize conservative modifications that maintain original phrasing over more substantial restructuring needed for effective simplification. the ranking across metrics illuminates the inherent tensions in text simplification. systems that aggressively modify text to achieve target readability levels may sacrifice meaning preservation, while those that prioritize semantic fidelity may fail to achieve sufficient simplification. the ensemble approaches appear to navigate these trade-offs more successfully than the single iterative method, possibly because of their ability to consider multiple perspectives during the simplification process. ## conclusion we presented two systems for proficiency-targeted simplification of texts and show that lexical resources can help guide simplification systems to achieve simplifications that better adhere to target levels. the comparative analysis of our three system variants reveals important insights about the architecture of controlled text simplification systems. the superior performance of ez-scalar lex demonstrates that hybrid approaches combining neural language models with established linguistic resources can outperform purely neural methods. this finding aligns with broader trends in natural language processing, where the integration of explicit knowledge representations with statistical learning has proven valuable across multiple tasks. the ensemble architecture employed in ez-scalar, which leverages multiple models through critique and refinement cycles, appears more robust than the iterative single-model approach of saga, suggesting that diversity in simplification strategies contributes more to quality than repeated self-correction. however, the modest performance gains and our mid-table ranking among participating teams indicate substantial room for improvement. future work should explore more sophisticated integration of lexical resources, potentially incorporating syntactic complexity measures alongside vocabulary targeting, and investigate whether larger-scale ensemble approaches or fine-tuned models specifically trained on cefr-leveled data could further enhance performance while maintaining the interpretability and controllability that explicit lexical guidance provides.