Large Language Models: A Survey academic paper (https://arxiv.org/pdf/2402.06196v1) offers a comprehensive overview of Large Language Models (LLMs), tracing their development from earlier language models and highlighting the impact of models like GPT, LLaMA, and PaLM. It details the methods of building LLMs, including data preparation, tokenization, and various pre-training and fine-tuning techniques, such as instruction tuning and alignment methods like RLHF and KTO. The text also explores how LLMs are utilized and enhanced, covering prompting strategies like Chain-of-Thought and Tree-of-Thought, and augmentation techniques like Retrieval Augmented Generation (RAG) and tool usage, which contribute to the development of LLM-based agents. Furthermore, it surveys popular datasets and benchmarks for evaluating LLM performance across different tasks like reasoning, coding, and world knowledge. Finally, the paper concludes by addressing current challenges and future directions in LLM research, including the pursuit of smaller, more efficient models, new architectural paradigms, and the rise of multi-modal LLMs.

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