The field of natural language processing is witnessing significant developments in various areas, including entity recognition and linking, tokenization and vocabulary management, and the integration of structured knowledge. Researchers are exploring innovative approaches to improve the accuracy and efficiency of these tasks, with a focus on leveraging large language models (LLMs) and reinforcement learning. Notable papers include Knowing the Facts but Choosing the Shortcut, which investigates the reliance of LLMs on genuine knowledge versus superficial heuristics, and PANER, which presents a paraphrase-augmented framework for low-resource named entity recognition. Additionally, the development of dynamic and hierarchical tokenization methods, such as those proposed in Vocab Diet and DVAGen, is improving the representational power of language models. The field of recommender systems is also moving towards more efficient and effective methods, with a focus on addressing biases in user feedback and ensuring fairness for content creators. Noteworthy papers include GRank, which presents a novel structured-index-free retrieval paradigm, and BPL, which introduces a bias-adaptive preference distillation learning framework. Furthermore, the integration of LLMs with collaborative filtering and sentiment analysis is leading to significant improvements in personalized recommendation systems and sentiment analysis applications. Overall, these advancements have the potential to drive significant improvements in the performance and generalizability of language models and recommender systems, and are likely to have a substantial impact on the field.