The field of natural language processing is witnessing significant advancements in relation extraction and named entity recognition. Researchers are exploring new approaches to improve the accuracy and efficiency of these tasks, particularly in scenarios where labeled data is scarce or unavailable. One notable direction is the development of methods that can effectively utilize large language models to generate high-quality training data, reducing the need for manual labeling and improving model performance. Another area of focus is the creation of more robust and generalizable models that can handle diverse and noisy data, including the development of ensemble learning methods and techniques for improving the diversity of generated training samples. Overall, these innovations have the potential to greatly enhance the capabilities of relation extraction and named entity recognition systems, enabling them to better support a wide range of applications. Noteworthy papers in this area include: EL4NER, which proposes an ensemble learning method for named entity recognition using multiple small-parameter large language models, and Label-Guided In-Context Learning for Named Entity Recognition, which introduces a new method that leverages training labels to improve in-context learning performance.