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Yup. I think this is one of the places where the sheen will come off after a while. While temperature can mix things up a little, we’ll quickly start to see ho
by swatcoder 4y ago
Yup. I think this is one of the places where the sheen will come off after a while.
While temperature can mix things up a little, we’ll quickly start to see how the depth of LLM output is tied to the depth of LLM input — and people’s prompts are not all that varied and deep at the end of the day.
We’ll start to notice signatures to each generation of model in its responses to tiringly common prompts — especially in stuff like blog spam, homework essays, email punchups, casual fiction, etc
There are certain poles that it gravitates to that end up reading like verbal tics or lazy ideas, and those will be more obvious as we begin to get inundated with generated content.
Careful prompt work can evade those poles and tics, but most people won’t have the skill or drive to bother with that.
- bgroat 4y ago>> Careful prompt work can evade those poles and tics, but most people won’t have the skill or drive to bother with that. I absolutely agree with this. Furthermore, I've come to believe that "careful prompt work" doesn't really save much time relative to just writing/coding the output I want. Especially after QA/edits
- flangola7 4y ago> Furthermore, I've come to believe that "careful prompt work" doesn't really save much time relative to just writing/coding the output I want. The real magic happens when you train a better AI using those perfected prompts.
- dingclancy 4y agoThere would be parameters to evade the obvious picks. temperature can actually go up to 2 in the API and you can also go use top_p < 1. but for the more out of this world stuff, this is where fine tuning will come in. The main model will always come in as vanilla anyway.