2025 EMNLP EMNLP 2025

HULAT-UC3M at TSAR 2025 Shared Task A Prompt-Based Approach using Lightweight Language Models for Readability-Controlled Text Simplification

Abstract

AbstractThis paper describes the participation of the HULAT-UC3M team in the TSAR 2025 Shared Task on Readability-Controlled Text Simplification. Our approach uses open and lightweight Large Language Models (LLMs) with different sizes, together with two strategies for prompt engineering. The proposed system has been tested on the trial data provided, and evaluated using the official metrics CEFR Compliance, Meaning Preservation, and Similarity to References. LLaMA 3 8B model with reinforced prompts was selected as our final proposal for submission, and ranking fourteenth according to the overall metric. Finally, we discuss the main challenges that we identified in developing our approach for this task.

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — readability-controlled generation
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio