CRF2: Context Reasoning Faithful Framework - Damasceno, Hugo Firmino; Rocha, Leonardo Sampaio (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 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.