HOPE at TSAR 2025 Shared Task Balancing Control and Complexity in Readability-Controlled Text Simplification (2025)

Abstract: This paper describes our submissions to the TSAR 2025 Shared Task on Readability-Controlled Text Simplification. We present a comparative study of three architectures a rule-based baseline, a heuristic-driven expert system, and a zero-shot generative T5 pipeline with a semantic guardrail. Our analysis shows a trade-off between the controllability of rule-based systems and the fluency of generative models. In this zero-shot setting, simpler, confined systems achieved superior meaning preservation scores compared to the more powerful but less predictable generative model. We present a diagnostic failure analysis on system outputs, illustrating how different architectures result in distinct error patterns such as under-simplification, information loss via heuristics, and semantic drift.

## introduction the shared task on readability-controlled text simplification at the fourth workshop on text simplification, accessibility, and readability (tsar 2025) @xcite requires systems to simplify a given text to a specified common european framework of reference (cefr) level while preserving meaning. this creates a conflict between reducing linguistic complexity and maintaining semantic fidelity. to evaluate the trade-off between generative power and controllability, we engineered three systems: a deterministic baseline, a heuristic-driven expert system, and a zero-shot generative pipeline based on t5. our core empirical observation is that in this zero-shot setting, constrained and interpretable approaches perform better than a powerful generative pipeline on official meaning preservation metrics. we explore the specific failure modes of each system, illustrating a definite trade-off between generative power and semantic controllability, and propose directions for future hybrid approaches. ## related work text simplification (ts) has transformed from early rule-based systems primarily focused on lexical and syntactic transformations @xcite to the current paradigm which is dominated by neural sequence-to-sequence models @xcite . models like t5 @xcite and bart @xcite , pre-trained on vast text corpora, have become the de facto standard, achieving state-of-the-art fluency when fine-tuned on task-specific data. however, a key challenge in modern ts is controllability @xcite . while large language models are proficient at fluent paraphrasing, guiding them to simplify to a precise readability level without sacrificing semantic fidelity remains an open problem. researchers have explored techniques like explicit target-level prompting, but models can still "hallucinate" or deviate from the underlying meaning. our work directly address the issue, questioning whether a powerful generative model utilized in a zero-shot setting is the effective tool for a task with strict semantic constraints, echoing findings in other domains where simpler models can be more robust @xcite . ## system descriptions we implemented three systems with increasing complexity to explore the trade-off between control and generative power. ## experimental setup data: we present results on the official tsar 2025 test set (200 instances; targets a2/b1). the human-simplified 'reference' texts were employed in the official scoring and in our diagnostic analysis. official metrics: we present the official au-torank composite score and its components as provided by the organizers. ## semantic guardrail: design and limitations we implemented an embedding-cosine guardrail (model: 'all-minilm-l6-v2'; @xmath0.75). its advantages are speed and ease of deployment, and it successfully screens out many gross semantic deviations. however, our analysis reveals its limitations: cosine similarity is an imperfect proxy for entailment. as seen in the example in ## conclusion our comparative analysis on the tsar 2025 shared task shows a clear trade-off between control and fluency in zero-shot readability-controlled text simplification. our findings empirically illustrates that in the absence of fine-tuning data, simpler, interpretable approaches can be more robust for semantic fidelity. a simple baseline preserved meaning but was insufficient for structural simplification. a heuristic-driven expert system enhanced readability metrics but caused significant information loss. finally, a generative pipeline offered fluent paraphrasing but was highly vulnerable to content omission, resulting in the lowest meaning preservation scores. this implies that for tasks with stringent semantic constraints, the controllability of simpler systems provides a distinct advantage. limitations: the primary limitation of this study is the lack of a large-scale human evaluation to confirm whether the automated metrics, including the official autorank, fully align with human judgments of simplification quality. while our analysis uses human-written references for scoring, it does not include direct human ratings of our systems' outputs. furthermore, our analysis revealed that the embedding-based guardrail, while effective at catching major deviations, is an imperfect proxy for fine-grained semantic fidelity.