@inproceedings(2026-elirf-upv-at-simpletext-2026-decoupling-action-prediction-and-controlled-generation-for-text-simplification,
  author = {Marc Hurtado-Beneyto and Vicent Ahuir Esteve and Antonio Molina Marco and María-José Castro-Bleda},
  title = {ELiRF-UPV at SimpleText 2026: Decoupling Action Prediction and Controlled Generation for Text Simplification},
  year = {2026},
  booktitle = {Working Notes of the Conference and Labs of the Evaluation Forum (CLEF 2026)},
  abstract = {In this work, we describe the ELiRF-UPV participation in the CLEF 2026 SimpleText Track, Simplify Scientific Text. Our approach for text simplification decouples the process into two stages: planning and generation. First, a model predicts sentence-level simplification actions, which are then used to guide the final simplification. For the generation stage, we explore and compare two primary strategies. The first one is LLM-based, where we apply prompting techniques. For the second strategy, we fine-tune an encoder-decoder-based automatic simplification model. This design allows the models to learn new actions without suffering from catastrophic forgetting of their pre-trained knowledge. In addition, we apply Direct Preference Optimization (DPO) training to strictly align the model's outputs with the predicted control instructions, mitigating unwarranted changes and ensuring accurate simplifications. A core principle of our work is its alignment with Green AI guidelines; by using small open-source models, we aim to democratize controlled text generation and reduce the high computational and environmental costs associated with massive proprietary models.}
)