The article "Natural Language Processing in the Era of Large Language Models" by Arkaitz Zubiaga provides an overview of the evolution and impact of large language models (LLMs) in the field of natural language processing (NLP). Since the 1980s, language models (LMs) have evolved from statistical approaches to more sophisticated models, with LLMs being introduced in recent years due to advancements in computational resources and text collection availability. LLMs, built on architectures like Transformer, have revolutionized NLP, achieving state-of-the-art performance in various tasks such as understanding, generation, and reasoning. However, the surge of LLMs has also raised concerns. Key limitations include the black box nature of models like ChatGPT, which lacks transparency and reproducibility, data contamination where test sets are used during training, biases in the training data, the generation of offensive content, privacy risks, imperfect accuracy, model hallucination (deviations from common sense), and lack of explainability. These issues highlight the need for further research to improve LLMs and address ethical and practical challenges. The article concludes by emphasizing the impact of LLMs on NLP research and the ongoing efforts to tackle their limitations, particularly in data curation and fairness. It also discusses the potential benefits of AI tools in supporting human labor rather than replacing it, as suggested by recent studies.The article "Natural Language Processing in the Era of Large Language Models" by Arkaitz Zubiaga provides an overview of the evolution and impact of large language models (LLMs) in the field of natural language processing (NLP). Since the 1980s, language models (LMs) have evolved from statistical approaches to more sophisticated models, with LLMs being introduced in recent years due to advancements in computational resources and text collection availability. LLMs, built on architectures like Transformer, have revolutionized NLP, achieving state-of-the-art performance in various tasks such as understanding, generation, and reasoning. However, the surge of LLMs has also raised concerns. Key limitations include the black box nature of models like ChatGPT, which lacks transparency and reproducibility, data contamination where test sets are used during training, biases in the training data, the generation of offensive content, privacy risks, imperfect accuracy, model hallucination (deviations from common sense), and lack of explainability. These issues highlight the need for further research to improve LLMs and address ethical and practical challenges. The article concludes by emphasizing the impact of LLMs on NLP research and the ongoing efforts to tackle their limitations, particularly in data curation and fairness. It also discusses the potential benefits of AI tools in supporting human labor rather than replacing it, as suggested by recent studies.