The recent developments in the research area of information operations and large language models (LLMs) have shown significant advancements in both data collection and model application. There is a notable shift towards creating comprehensive datasets that include control data, enabling more robust detection algorithms for information operations. These datasets facilitate the study of narratives and network interactions, crucial for understanding and countering manipulation campaigns on social media. Additionally, LLMs are increasingly being applied to relational databases, demonstrating competitive performance on predictive tasks despite the complexity of such databases. This opens new avenues for leveraging LLMs in data-intensive fields. Notably, innovative frameworks are being developed to enhance LLMs' performance in specific tasks like text-to-SQL conversion by incorporating continual learning mechanisms, which mimic human learning processes. These frameworks show promise in improving model accuracy and adaptability. Lastly, there is a growing emphasis on creating high-quality datasets for forecasting future international events, which can significantly impact global policy and strategic decision-making. These datasets, enriched with expert validation, are poised to drive advancements in text-based event prediction.