This has nothing on it about llms, where in context learning, retrieval augmention, tool using, and multi modality is really blazing ground. Ideally we'd have p

This has nothing on it about llms, where in context learning, retrieval augmention, tool using, and multi modality is really blazing ground. Ideally we'd have papers about these methods, because we need these tools to implement stuff, and they're changing how we do business. Without in context learning, self critique and tool using, for example, it's easy to think that LLMs are stochastic parrots bound by their training distribution, instead of last mile learners that can adapt to out of distribution contexts. And business applications need RAG to overcome hallucinations. Without these concepts, I don't think people will really know 90% of what matters today, or be ready to take on what matters tomorrow. I could be wrong here, with my focus on so many llm specific topics. Things like in context learning and RAG exist as engineering workarounds for LLM limitations. They are not definite paths to tomorrow's AI, which won't need our cajoling and hand holding. There's a possibility that we'll move past them, so more speculative and theoretical papers are better. But I think there's also a possibility that the AI scaling is done for this AI bubble, and we're stuck with building larger scale systems out of imperfect buggy LLMs for the foreseeable future. In that case, maybe some next generation Zen of integrating LLMs into a working mind eventually emerges as a way forward.. so we should pay attention to engineering efforts around LLMs!