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Short Review of LLM
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These two sources (https://arxiv.org/pdf/2402.06196v1 and https://arxiv.org/pdf/2303.18223) provide comprehensive surveys of the field of Large Language Models (LLMs). They cover the foundational aspects, starting with the background and evolution of language models, highlighting the significance of scaling and emergent abilities in LLMs, particularly Transformer-based models.
The surveys detail how LLMs are built, including the crucial steps of pre-training on massive datasets, discussing data preparation methods like filtering and tokenization. They also delve into adaptation techniques such as Instruction Tuning and Reinforcement Learning with Human Feedback (RLHF) to align models with specific tasks or human preferences.
Furthermore, the papers describe how LLMs are utilized through strategies like prompting and In-Context Learning (ICL), including methods like Chain-of-Thought prompting. A significant portion is dedicated to capacity evaluation, reviewing various benchmarks and metrics used to assess abilities like language generation, knowledge utilization, and reasoning, while also addressing challenges like hallucination. Topics like Retrieval-Augmented Generation (RAG) and available resources are also covered.
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