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Discussion: Discussion Thread for comments, corrections, or any feedback. Summary: The latest batch of language models can be much smaller yet achieve GPT-3 like performance by being able to query a database or search the web for information. A key indication is that building larger and larger models is not the only way to improve performance. The last few years saw the rise of Large Language Models (LLMs) – machine learning models that rapidly improve how machines process and generate language. Some of the highlights since 2017 include: The original Transformer breaks previous performance records for machine translation. BERT popularizes the pre-training then finetuning process, as well as Transformer-based contextualized word embeddings. It then rapidly starts to power Google Search and Bing Search. GPT-2 demonstrates the machine’s ability to write as well as humans do. First T5, then T0 push the boundaries of transfer learning (training a model on one task, and

Discussion threadLarge language modelsGoogle searchImproving language modelsSeparating language informationWorld knowledgeTransformer encoderIllustrated transformerChunked cross attention