Natural language processing (NLP) has evolved significantly with the advent of large language models (LLMs). Since their inception in the 1980s, language models have been used to statistically model natural language properties. LLMs, introduced in recent years, have revolutionized NLP by leveraging large text collections to train models that can perform tasks such as understanding, generation, and reasoning. These models are pre-trained on extensive datasets and can be fine-tuned for specific applications. However, their use has raised concerns regarding data contamination, bias, privacy, and the potential for generating offensive content. LLMs have demonstrated state-of-the-art performance in various NLP tasks, becoming the de facto baseline for many experiments. However, they also pose ethical challenges, including the risk of being used for malicious purposes, such as academic cheating or spreading misinformation. Additionally, the black-box nature of LLMs raises issues regarding transparency and reproducibility, prompting research into methods for reverse engineering these models. Open-source models have emerged as a solution to some of these challenges, offering transparency and fairness. Nevertheless, LLMs still face limitations such as bias, hallucination, and lack of explainability. Efforts are ongoing to address these issues through improved data curation, model evaluation, and mitigation strategies. The future of LLMs will depend on addressing these challenges to ensure they are used ethically and effectively in society.Natural language processing (NLP) has evolved significantly with the advent of large language models (LLMs). Since their inception in the 1980s, language models have been used to statistically model natural language properties. LLMs, introduced in recent years, have revolutionized NLP by leveraging large text collections to train models that can perform tasks such as understanding, generation, and reasoning. These models are pre-trained on extensive datasets and can be fine-tuned for specific applications. However, their use has raised concerns regarding data contamination, bias, privacy, and the potential for generating offensive content. LLMs have demonstrated state-of-the-art performance in various NLP tasks, becoming the de facto baseline for many experiments. However, they also pose ethical challenges, including the risk of being used for malicious purposes, such as academic cheating or spreading misinformation. Additionally, the black-box nature of LLMs raises issues regarding transparency and reproducibility, prompting research into methods for reverse engineering these models. Open-source models have emerged as a solution to some of these challenges, offering transparency and fairness. Nevertheless, LLMs still face limitations such as bias, hallucination, and lack of explainability. Efforts are ongoing to address these issues through improved data curation, model evaluation, and mitigation strategies. The future of LLMs will depend on addressing these challenges to ensure they are used ethically and effectively in society.