February 28, 2025

1. Introduction Ever since the introduction of the self-attention mechanism, Transformers have been the top choice when it comes to Natural Language Processing (NLP) tasks. Self-attention-based models are highly parallelizable and require substantially fewer parameters, making them much more computationally efficient, less prone to overfitting, and easier to fine-tune for domain-specific tasks [1]. Furthermore, the key advantage of transformers over past models (like RNN, LSTM, GRU and other neural-based architectures that dominated the NLP domain

Introduction In a YouTube video titled Deep Dive into LLMs like ChatGPT, former Senior Director of AI at Tesla, Andrej Karpathy discusses the psychology of Large Language Models (LLMs) as emergent cognitive effects of the training pipeline. This article is inspired by his explanation of LLM hallucinations and the information presented in the video. You might have seen model hallucinations. They are the instances where LLMs generate incorrect, misleading, or entirely fabricated information that appears plausible. These hallucinations

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