UC-UCO-CICESE_UT3-Plenitas Team - Exploring in the PROFE 2025: Language Proficiency Evaluation

PROFE2025 is a challenge that tests automated systems on authentic Spanish reading-comprehension exams used by the Instituto Cervantes for human learners, without providing task-specific training data. This setup emphasizes transfer learning and large generative models. Participants can choose from three subtasks: Multiple Choice, Matching, and Fill-in-the-Gap, each evaluated using accuracy metrics that align with human assessment. Teams completing all subtasks receive an exam-level score based on the proportion of exams passed (60% accuracy), enabling direct comparison to human performance. The competition utilizes the IC-UNED-RC-ES corpus and Hugging Face's transformer model ecosystem. In internal evaluations, three Qwen-3 variants were tested: Qwen 3.0, a "think" prompting version, and a DeepSeek-distilled Qwen 2.5 7B. Both base and distilled models achieved 32.22% overall accuracy, while the "think" variant scored only 10.19%, indicating room for improvement in reasoning-focused prompts. Among 40 submissions, only two teams completed all three subtasks. UC-CICESE_UT3-Plenitas placed second with 32.22%, 4.89%, and 10.19%, respectively, underscoring both potential and current limitations of LLMs in this domain. PROFE2025 highlights that large language models can engage meaningfully with complex, human-like exam conditions, but significant improvements are possible through specialized adaptation, fine-tuning, and advanced prompting strategies, positioning it as a key benchmark for progress in automated language understanding..