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I assume you are referring to transformers, which came out in the literature around 2017. Attention on its own is significantly older, but wasn't really used in a context that came close to being used as a large language model until the early / mid 2010s.
While attention is fairly simplistic, a trait which helps it parallelize well and scale well, there is a lot of research that came about recently around how the text is presented to the model, and the size of the models. There is also a lot of sophistication around instruction tuning and alignment as well which is how you get from simple text continuation to something that can answer questions. I don't think you could make something like chatGPT using just the 2017 "Attention is All You Need" paper.
I suspect that publicly released models lags whatever google or OpenAI has figured out by 6 months to a year, especially because there is now a lot of shareholder pressure around releasing LLM based products. Advancement that are developed in the open source community, like apply LoRA and quantization in various contexts, has a significantly shorter time between development and release.
I stand corrected. Thank you for educating me. I just assumed that previous NLP algorithms were not too far off from LLM in terms of capabilities, which is apparently not the case.
I'm finding this article helpful in understanding LLMs.