STARLING at TSAR 2025 Shared Task Leveraging Alternative Generations for Readability Level Adjustment in Text Simplification (2025)

Abstract: Readability adjustment is crucial in text simplification, as it allows to generate language appropriate to the needs of a particular group of readers. Here we present a method for simplifying a text fragment that aims for a given CEFR level, e.g. A2 or B1. The proposed approach combines prompted large language model with sentence-level adjustment of difficulty level. The work is evaluated within the framework of TSAR 2025 shared task, showing a trade-off between precise readability adjustment and faithful meaning preservation.

## task our solution was prepared within the framework of the tsar 2025 shared task on readability-controlled text simplification @xcite . the task participants were provided with a series of pairs, each including a text fragment in english (paragraph-length) and a cefr level, and their goal was to provide simplifications that maintain the meaning of the original fragment, but at the same time possess the provided difficulty level (a2 or b1). the submitted fragments were evaluated using the following measures: of these, the rmse cefr and both meaning-bert similarities were used to establish the final ranking. for details regarding the previous work in the domain, evaluation details and results of all participants, see the overview article @xcite . ## methods the submitted solution was prepared using elements of starling (simplifying text across languages using generative models): a ts system under construction, which uses llm prompting for obtaining robust simplifications across several languages, including some with low nlp support. the multilingual capabilities were however not used here, since the task is performed in english, a language with sufficient monolingual resources. broadly, the solution involves the following steps: ## evaluation the solution described above is our baseline approach to the problem. however, in order to better understand its strengths and weaknesses, we check the result of modifying some aspects: all the solutions are tested using the evaluation code provided by the organisers (see section 2) on the 40 instances of the trial set. the full results on the test dataset, including our submission, can 5 https://huggingface.co/abdullahbarayan/ modernbert-base-doc_en-cefr , https://huggingface. co/abdullahbarayan/modernbert-base-doc_ sent_en-cefr and https://huggingface.co/ abdullahbarayan/modernbert-base-reference_ alllang2-cefr2 be found in the shared task overview article @xcite . ## discussion judging by the evaluation performed in the previous section, our approach performs well: it allows to adjust for the desired readability level without sacrificing too much of the original meaning. however, this is clearly a prototype solution and several limitations remain. firstly, a more intensive adjustment to readability level is possible. since generating 10 variants allows better compliance than 5, one could also try 20 or 100 -though in case of most sentences it would be unrealistic to expect that many different reformulations. it is therefore an open question on whether this wouldn't cause a loss of meaning or general decrease in quality of further generations. we leave this for future work. secondly, we limited our prompting experiment to adapting a formulation that has been found to work well in previous research, but that usecase did not include readability adjustment. therefore, we expect that experimenting with the prompt can help to guide the model towards producing reformulations with more utility for the current setup, e.g. by encouraging diveristy in the variants. recent research in ts indicates great room for improvement in tuning the prompt @xcite . thirdly, we performed the simplification on the sentence level to obtain fine-grained control over the variants chosen and avoid the loss of details that we observe in full paragraph rewriting. however, this introduces a limitation: when each sentence is reformulated independently, we risk breaking discourse links between them, resulting in less coherent text. this is a known problem in text simplification, but it requires further research to deliver satisfactory solutions @xcite . one bypass could be to provide whole paragraphs as input text and look for other ways to avoid information loss, e.g. through various prompts. finally, we have to emphasise that our evaluation is based solely on automatic measures, which for some time have been known to poorly reflect human judgement of simplified text @xcite . this final step of ts evaluation should involve human evaluators, especially when a solution is claimed to be adjusted to specific user group. ## conclusions to sum up, the proposed approach to readabilityadjusted text simplification achieves positive results, delivering output that is in agreement with the desired cefr level (95% accuracy) and maintains the overall meaning (93% bertscore). depending on which configuration we choose, a trade-off between simplicity and similarity to the original can be struck differently, but overall the results indicate this to be a promising direction for future investigation. the european union's horizon europe research and innovation programme under grant agreement no 101178191. views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the european union. neither the european union nor the granting authority can be held responsible for them. we gratefully acknowledge polish high-performance computing infrastructure plgrid (hpc centers: ack cyfronet agh) for providing computer facilities and support within computational grant no. plg/2025/018019.