CRF2: Context Reasoning Faithful Framework - Damasceno, Hugo Firmino; Rocha, Leonardo Sampaio (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2025: Anais do XXII Encontro Nacional de Inteligência Artificial e ComputacionalDO - 10.5753/eniac.2025.14515, 2025)
Large Language Models (LLMs) excel in tasks like text generation and question answering, but often rely on parametric knowledge, leading to hallucinations when required to reason strictly within a given context. In this paper, we propose a conceptual framework for context-faithful reasoning, ensuring responses are grounded solely in the provided information. We evaluate our approach using the RealTime QA dataset, which features open-domain, time-sensitive questions that require grounding in a specific document. Our experiments demonstrate that the proposed method outperforms existing prompt engineering techniques on abstention tasks, achieving an accuracy of 0.95 compared to baseline models.