U o L - UPF at TSAR 2025 Shared Task A Generate-and-Select Approach for Readability-Controlled Text Simplification (2025)

Abstract: The TSAR 2025 Shared Task on Readability-Controlled Text Simplification focuses on simplifying English paragraphs written at an advanced level (B2 or higher) and rewriting them to target CEFR levels (A2 or B1). The challenge is to reduce linguistic complexity without sacrificing coherence or meaning. We developed three complementary approaches based on large language models (LLMs). The first approach (Run 1) generates a diverse set of paragraph-level simplifications. It then applies filters to enforce CEFR alignment, preserve meaning, and encourage diversity, and finally selects the candidates with the lowest perceived risk. The second (Run 2) performs simplification at the sentence level, combining structured prompting, coreference resolution, and explainable AI techniques to highlight influential phrases, with candidate selection guided by automatic and LLM-based judges. The third hybrid approach (Run 3) integrates both strategies by pooling paragraph- and sentence-level simplifications, and subsequently applying the identical filtering and selection architecture used in Run 1. In the official TSAR evaluation, the hybrid system ranked 2nd overall, while its component systems also achieved competitive results.

## our pipeline our submission consists of three systems (runs 1-3). our core approach, which achieved 3rd place overall, is presented as run 1. while our primary objective is to achieve a high output-to-reference similarity, we also aim to attain satisfactory scores in other metrics, namely cefr compliance and meaning preservation. ## results and discussions the hybrid system (run 3) is ranked 2nd, while the core system (run 1) is 3rd overall. furthermore, our systems placed 1st (tied, full marks) on cefr alignment, and 1st and 2nd on output-to-reference similarity. this result confirms the success of our pipeline combining filtering and mbr decoding, thereby achieving the high output-to-reference similarity while maintaining other metrics. furthermore, we conducted ablation study shown in this success also highlights an important characteristic of our method. figure 2 illustrates the meaningbert scores distribution of cefraligning candidates for one example instance. while the final output shows the highest output-tooriginal similarity, several candidates show higher output-to-reference similarity. this observation confirms that mbr decoding is designed to minimize the risk of selecting a low-scoring candidate, not to select one with the maximum expected score. as a result, final outputs are often conservative. despite prioritizing output-to-reference similarity, we acknowledge that over-reliance on this metric can be problematic. our qualitative analysis shows limited agreement between scores and human judgments. specifically, instances containing semantic errors or complex vocabulary (yellow in the scatter plot) are often over-evaluated by the metric when they are structurally similar to the reference. on the other hand, structure changes, such as sentence splitting, are penalized even if beneficial. our case study supports that meaningbert often fail to capture the value of features such as sentence splitting, synonym choice, and moral or pragmatic clarity, rewarding surface overlap instead of genuine accessibility @xcite . we provide full analysis in appendix d. ## conclusion in this paper, we presented our generate-and-select framework for the tsar 2025 shared task, which achieved 2nd and 3rd place overall. our core approach utilized a diverse candidate pool from multiple llms and prompts, with mbr decoding for robust selection. our primary contribution is demonstrating that our generate-and-select framework is highly effective. we showed that its strength lies in prioritizing the diversity of candidates, which allowed even a weaker system (our sentence-level run 2) to make a contribution to the final performance by injecting variety. finally, our analysis shows that while our pipeline is robust, its limitation in a singlereference context highlights the need for selection methods that can better handle unpredictable simplifications.