Explainable AI in Large Language Models: A Review

Explainable AI in Large Language Models (LLMs) represents an exciting frontier in Artificial Intelligence. In recent times, LLMs provides various AI applications ranging from chatbots to content generation. While these applications are exciting, their decision-making process behind the intelligent systems plays a major role. These processes are also a mystery and they operate as "Black boxes", where the processes are often challenging to interpret. This paper helps to give a sense of the processes that occurs behind these decisions. It helps to understand why the AI has chosen one sentence rather than the other. It explores key breakthroughs of XAI techniques that helps in solving these complexities. Methods such as Attention Visualization, Feature Importance Analysis and Counterfactual Explanations provide insights on why certain decisions are made. These techniques provide a voice to the intricate processes and address real-world challenges, which makes it more reliable. This enables better transparency and makes it trust-worthy. XAI has the potential to address ethical concerns, enhance user trust and improve Human-AI collaboration. This research highlights how better transparency in AI is not merely a technical challenge but a foundational step towards building a future where humans and AI work together seamlessly and responsibly.