I’ve been fascinated by this paper: Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agent and have been trying out the ideas with success in a few personal projects. They essentially found that GPT4 is excellent at ranking things. Given a set of items (candidates) and some query it can rank these items very well (as well as or better than the current SOTA models for the benchmarks they evaluated)....
The code for this post is here. The second experiment I’ve tried is GPT3 to extract and structure data and have been pretty impressed. The below example took me about an hour to setup, most of it just being iterating on the prompts I’m using as directions to the model. An additional bonus I wasn’t expecting - this also turned out to be a decent recipe generator. If I input in just a recipe name like Pumpkin Pie it’ll generate/hallucinate structured ingredients and instructions....
The code for this post is here. I’ve been playing around with GPT3 (and ChatGPT) a lot and have been wanting to start posting again, so I decided to start posting little experiments I do. These won’t be polished but hopefully interesting at least. Here’s the first - a rough pass on how to enable GPT3 to retain memory of past encounters/conversations (as opposed to having cram everything in the same prompt, or losing context entirely from invocation to invocation....