The field of natural language processing is rapidly advancing, with a focus on improving the performance and privacy of large language models (LLMs). Recent research has highlighted the potential vulnerabilities of LLMs to privacy breaches, and the need for robust defense mechanisms. In response, researchers are developing innovative approaches to data extraction, privacy-preserving translation, and economic statistic estimation. These advances have the potential to enable more efficient and accurate language translation, as well as improved estimation of economic and financial statistics. Notably, researchers are also exploring the use of inverse reinforcement learning and transfer learning to improve the performance of LLMs. Noteworthy papers include: DMRL, which proposes a data- and model-aware reward learning approach for data extraction from LLMs. Revealing economic facts, which demonstrates that the hidden states of LLMs can be used to estimate economic and financial statistics.