In terms of LLMs what we're seeing in newer research are following trends: 1. Scaling up LLMs only work if you scale up the data. 2. Chain of Thought promptin

In terms of LLMs what we're seeing in newer research are following trends: 1. Scaling up LLMs only work if you scale up the data. 2. Chain of Thought prompting helps it improve accuracy. Teaching it how to solve similar problem in steps and then showing it how to answer full problem. 3. LLMs are great at translation. e.g translating to code / sql. Interfacing LLM to a knowledgebase / python repl / physics engine also improves it's accuracy. I'd have to cite a bunch of papers on arxiv if anyone is interested. Stephen Wolfram is on spot that marrying LLMs to symbolic computation is the holy grail. E.g the avatar generators are able to tap into latent nodes for a certain face/body and use that to generate other images. I'm sure as neural architectures evolve, we'll see more symbolic computation in neural networks aka neurosymbolic AI. The hard part is having computers abstract and figure out the symbolic representations by themselves instead of an army of humans carefully building databases and code.