Here's a bunch of other related methods,
Summarizing context - https://arxiv.org/abs/2305.14239
continuous finetuning - https://arxiv.org/pdf/2307.02839.pdf
retrieval augmented generation - https://arxiv.org/abs/2005.11401
knowledge graphs - https://arxiv.org/abs/2306.08302
augmenting the network a side network - https://arxiv.org/abs/2306.07174
another long term memory technique - https://arxiv.org/abs/2307.02738
However there's been some work that to "get extra milage" out of the current models so-to speak with rotary positions and a few other tricks. These in combination with finetuning is the current method many are using at the moment IIRC.
Here's a decent overview https://aman.ai/primers/ai/context-length-extension/
Rope - https://arxiv.org/abs/2306.15595
Yarn (based on rope) - https://arxiv.org/pdf/2309.00071.pdf
LongLoRA - https://arxiv.org/pdf/2309.12307.pdf
The bottleneck is quickly going to be inference. Since the current transformer models need the context length ^2, the memory requirements go up very quickly. IIRC a 4090 can _barely_ fit a 4bit 30B model in memory with 4096k context length.
From my understanding some form of RNNs are likely to be the next step for longer context. See RWKV as an example of a decent RNN https://arxiv.org/abs/2305.13048
Like a Topic-based Personality Construct where the model first determines which of its “selves” should answer the question, and then grabs appropriate context given the situation.
There is nothing stopping someone from keeping an LLM in online-training mode forever. We don't do that because it's economically infeasible, not because it wouldn't work.