▲ | griomnib 4 days ago | ||||||||||||||||||||||||||||||||||
Pretty much the same, I work on some fairly specific document retrieval and labeling problems. After some initial excitement I’ve landed on using LLM to help train smaller, more focused, models for specific tasks. Translation is a task I’ve had good results with, particularly mistral models. Which makes sense as it’s basically just “repeat this series of tokens with modifications”. The closed models are practically useless from an empirical standpoint as you have no idea if the model you use Monday is the same as Tuesday. “Open” models at least negate this issue. Likewise, I’ve found LLM code to be of poor quality. I think that has to do with being a very experienced and skilled programmer. What the LLM produce is at best the top answer in stack overflow-level skill. The top answers on stack overflow are typically not optimal solutions, they are solutions up voted by novices. I find LLM code is not only bad, but when I point this out the LLM then “apologizes” and gives better code. My worry is inexperienced people can’t even spot that and won’t get this best answer. In fact try this - ask an LLM to generate some code then reply with “isn’t there a simpler, more maintainable, and straightforward way to do this?” | |||||||||||||||||||||||||||||||||||
▲ | blharr 4 days ago | parent | next [-] | ||||||||||||||||||||||||||||||||||
There have even been times where an LLM will spit out _the exact same code_ and you have to give it the answer or a hint how to do it better | |||||||||||||||||||||||||||||||||||
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▲ | david-gpu 4 days ago | parent | prev | next [-] | ||||||||||||||||||||||||||||||||||
> I’ve found LLM code to be of poor quality Yes. That was my experience with most human-produced code I ran into professionally, too. > In fact try this - ask an LLM to generate some code then reply with “isn’t there a simpler, more maintainable, and straightforward way to do this?” Yes, that sometimes works with humans as well. Although you usually need to provide more specific feedback to nudge them in the right track. It gets tiring after a while, doesn't it? | |||||||||||||||||||||||||||||||||||
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▲ | Sharlin 4 days ago | parent | prev [-] | ||||||||||||||||||||||||||||||||||
> In fact try this - ask an LLM to generate some code then reply with “isn’t there a simpler, more maintainable, and straightforward way to do this?” These are called "code reviews" and we do that amongst human coders too, although they tend to be less Socratic in nature. I think it has been clear from day one that LLMs don't display superhuman capabilities, and a human expert will always outdo one in tasks related to their particular field. But the breadth of their knowledge is unparalleled. They're the ultimate jacks-of-all-trades, and the astonishing thing is that they're even "average Joe" good at a vast number of tasks, never mind "fresh college graduate" good. The real question has been: what happens when you scale them up? As of now it appears that they scale decidedly sublinearly, but it was not clear at all two or three years ago, and it was definitely worth a try. |