3,064 karma · joined February 23, 2008
Unfortunately, it is unlikely we will ever get something like a Semantic web. It seemed like a good idea in the beginning of 2000s but now there is honestly no need for it as it is quite cheap and easy to attach meaning to text due to the progress in LLMs and NLP.
It is a bit hard to get it to work reliably except for small changes.
This is what happens if you try directly.
You: Please count the number of t in eeooeotetto.
ChatGPT: There are 5 t's in "eeooeotetto".
The short answer to how abilities like in-context learning and chain—of-thought prompting emerge is that we don’t really know. But for instruction-tuned models you can see that the dataset usually has a fixed set of tasks and the initial prompt of “You are so and so” helps model align it to follow instructions. I believe the datasets are this way because they were written by humans to help others answer instructions in this manner.
Others have also pointed out how RLHF may also be the reason why most prompts look like this.
When using the analysis a PR for upgrading the dependencies would look like this - https://github.com/tmroberts56/java-maven/pull/3
A short handbook on developing high quality software in the DevOps world.
It uses separation logic which is an extension of Hoare logic for programs that manipulate the heap.