That's the problem I've been facing with Llama 2 as well. It's almost impossible to have it just output the desired text. It will always add something before and after its response. Does anyone know if there's any prompt technique to fix this problem?
airoboros supports the PLAINFORMAT token "to avoid backticks, explanations, etc. and just print the code".
https://huggingface.co/TheBloke/airoboros-l2-70B-GPT4-2.0-GG...
I wonder if LLMs will have less reasoning power if they simply return the output. AFAIK, they think by writing their thoughts. So forcing an LLM to just return the goddamn code might limit its reasoning skills, leading to poor code. Is that true?
In practice I haven't seen it make too much of a difference with GPT. The model can still use comments to express itself.
For non coding tasks, adding "Think step by step" makes a huge difference (versus YOLOing a single word reply).
Yes you're right. I'm mostly concerned with the text that actually "computes" something before the actual code begins. Niceties like "sure! happy to help" don't compute anything.
CoT indeed works. Now I've seem people take it to the extreme by having tree of thoughts, forest of thoughts, etc. but I'm not sure how much "reasoning" we can extract from a model that is obviously limited in terms of knowledge and intelligence. CoT already gets us to 80% of the way. With some tweaks it can get even better.
I've also seen simulation methods where GPT "agents" talk to each other to form better ideas about a subject. But then again, it's like trying to achieve perpetual motion in physics. One can't get more intelligence from a system than one puts in the system.
Not necessarily the same thing, as you're still putting in more processing power/checking more possible paths. Its kinda like simulated annealing, sure the system is dumb, but as long as checking if you have a correct answer is cheap, it still narrows down the search space a lot.
Yeah I get that. We assume there's X amount of intelligence in the LLM and try different paths to tap on that potential. The more paths are simulated, the closer we get to the LLM's intelligence asymptote. But then that's it—we can't go any further.
There's no reason to handle the LLM side of things, unless you want to try and optimize the amount of tokens which are code vs comments vs explanations and such. (Though you could also just start a new context window with only your code or such)