In context learning is hands down the biggest breakthrough of LLMs. The flexibility the model displays without updating weights is genuinely mind blowing, bordering on absurd especially if you've trained other kinds of models before. See here - https://imgur.com/a/w3DAYOi from the paper - https://arxiv.org/abs/2211.09066 GPT 3.5's (4 is much much better) addition accuracy tanks after 2 digits. However, by approaching…
I've noticed if I give chat GPT an algorithm it gives me consistent results when it shows it's work but if I ask it not to show it's work and just give me the answer. It consistently gives me incorrect answers even when I ask it to follow an algorithm that I gave it. I suppose this is similar to humans and probably why my school teachers always told me to show my work, but I'm curious if this has been documented and…
By their very nature they only "know" what they have written down and must infer the final answer from that token by token.
They fundamentally can't do certain things such as complex iteration or track back.
When you ask for chain of thought thinking, you allow the LLM to create a "buffer space" and break down the task into more manageable substeps thereby improving the quality of the results.