Moving forward, future research directions include refining language models specifically optimized for materials science literature through fine-tuning and domain-specific training. Additionally, exploring strategies such as active learning, where the model interacts with the user to identify areas requiring further annotation, and integrating domain knowledge bases to provide context-specific informa-tion, could enhance the efficiency and accuracy of the annotation process. These advancements aim to reduce the need for extensive human intervention while ensuring the creation of high-quality annotated datasets essential for training and improving language models tailored to the materials science domain. By pursuing these avenues for improvement, we can continue to advance semi-automated annotation techniques, ultimately facilitating more efficient and accurate data extraction in materials science research.