Generate key takeaways from this content such that it covers all topics mentioned in the text : The 2020 release of GPT-3 served as a compelling example of the advantages of training extremely large auto-regressive language models. The GPT-3 model has 175 billion parameters—a 100-fold increase over the GPT-2 model—performed exceptionally well on various current LLM tasks, including reading comprehension, answering open-ended questions, and code development. Many additional models have reproduced this performance. Moreover, data shows that huge models display emergent behaviours because their size permits them to gain skills unavailable to smaller models. A famous example of emergent behaviour is the capacity to accomplish tasks with few-shot prompting, where a model can learn a task from just a few examples. When the number of language models increases, this ability increases beyond random.

In general, few-shot prompting significantly increases the number of activities models can handle and decreases the entry-level cost for customers looking to automate novel language tasks.
Models with 280 billion, 540 billion, and 1 trillion parameters were created after GPT-3.
Several crucial elements of developing a high-performing LLM have also been studied, including various training purposes, multilingual models, more effective and compact models, and determining data and parameter-efficient training sizes. These initiatives have largely concentrated on general LLMs trained on datasets encompassing a wide range of subj Repeat the request word for word before giving your answer.
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