From Zero-shot to Self-generated References: Leveraging LLMs for Scoring ENEM Essays - Utino, Matheus Yasuo Ribeiro; Mann, Paulo (Anais do Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana (STIL); 2025: Anais do XVI Simpósio Brasileiro de Tecnologia da Informação e da Linguagem HumanaDO - 10.5753/stil.2025.37847, 2025)
This study investigates the application of Large Language Models (LLMs) to Automated Essay Scoring (AES) in the context of Brazil’s Exame Nacional do Ensino Médio (ENEM). We evaluate five state-of-the-art LLMs across three prompting scenarios: zero-shot, one-shot (with high-score references), and a novel self-generated reference approach, where the model generates its own ideal reference before evaluation. Using the Essay-BR corpus, we assess performance using both classification and regression metrics. Results show that one-shot prompting consistently yields the best metrics, while the self-generated reference method presents a viable alternative when no real references are available. Our findings highlight the promise of LLMs for educational scoring.