In some domains (math and code), progress is still very fast. In others it has slowed or arguably stopped.
We see little progress in "soft" skills like creative writing. EQBench is a benchmark that tests LLM ability to write stories, narratives, and poems. The winning models are mostly tiny Gemma finetunes with single-digit parameter counts. Huge foundation models with hundreds of billions of parameters (Claude 3 Opus, Llama 3.1 405B, GPT4) are nowhere near the top. (Yes, I know Gemma is a pruned Gemini). Fine-tuning > model size, which implies we don't have a path to "superhuman" creative writing (if that even exists). Unlike model size, fine-tuning can't be scaled indefinitely: once you've squeezed all the juice out of a model, what then?
OpenAI's new o1 model exhibits amazing progress in reasoning, math, and coding. Yet its writing is worse than GPT4-o's (as backed by EQBench and OpenAI's own research).
I'd also mention political persuasion (since people seem concerned about LLM-generated propaganda). In June, some researchers tested LLM ability to change the minds of human subjects on issues like privatization and assisted suicide. Tiny models are unpersuasive, as expected. But once a model is large enough to generate coherent sentences, persuasiveness kinda...stops. All large models are about equally persuasive. No runaway scaling laws are evident here.
This picture is uncertain due to instruction tuning. We don't really know what abilities LLMs "truly" possess, because they've been crippled to act as harmless, helpful chatbots. But we now have an open-source GPT-4-sized pretrained model to play with (Llama-3.1 405B base). People are doing interesting things with it, but it's not setting the world on fire.