Summary Large Language Models (LLMs), based on the transformer architecture, like GPT, T5, and BERT, have achieved state-of-the-art (SOTA) results in various Natural Language Processing (NLP) tasks especially for text generation and conversational bots. The conventional paradigm is large-scale pretraining on generic web-scale data (CommonCrawl), followed by fine-tuning to downstream tasks. Fine-tuning these pretrained LLMs …
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