You can create chat-like situations with appropriate prompts.
For example, if I enter the message "Wow this day was horrible" to the chat application, it could translate it into a prompt like "Amy: Wow this day was horrible\nMe: " and the language model could auto-complete the response of me - assuming the language model was trained on chat logs between my friend Amy and me.
But as sigmoid10 mentioned, the quality of the replies will not be remotely comparable to ChatGPT.
True, true. But I already get somewhat coherent sentences that are personally enjoyable and valuable.
The main problem is that 90% of the output is garbage: broken formatting, abruptly ending sentences, mixed languages, occasional nonsensical sentences like `your "two" type,ir zwe "G together"`. If I can produce something that looks like normal sentences even if they're not particularily meaningful, with ~90% reliability, that's all I need for my purposes, and I think the current bottleneck is actually the very poor dataset of my very heterogeneous notes.
I'm thinking about it like how some people love their pets or toddlers even though they struggle with the most basic tasks. The personal connection makes up for it :)
Author here :) Some results (around 50%) are from a NN trained on 1.77MB of my notes, and others are from another NN trained on 1.25MB of my notes and 2MB of a random slice of "openwebtext" (OpenAI's WebText dataset from GPT2).
For example, if I enter the message "Wow this day was horrible" to the chat application, it could translate it into a prompt like "Amy: Wow this day was horrible\nMe: " and the language model could auto-complete the response of me - assuming the language model was trained on chat logs between my friend Amy and me.
But as sigmoid10 mentioned, the quality of the replies will not be remotely comparable to ChatGPT.