The research area of relation extraction and hallucination detection in synthetic data is experiencing significant advancements, particularly in the development of methods to mitigate the negative impacts of hallucinations on model performance. Recent studies have focused on enhancing the quality of synthetic datasets by detecting and removing hallucinations, thereby improving the robustness and generalization of relation extraction models. Innovations in retrieval-augmented generation (RAG) models have also been prominent, with new approaches aimed at better integrating external knowledge to reduce hallucinations. Additionally, there is a growing emphasis on controlled synthetic data generation for hallucination detection, which promises to provide more accurate and task-specific datasets. These developments collectively aim to refine the accuracy and reliability of models in extracting relations from text, with a particular focus on knowledge-intensive tasks. Notably, the introduction of differentiable data rewards in RAG systems represents a significant step forward in optimizing the interaction between different RAG modules, enhancing the overall performance of these models.