4 ms·
Probably because it isn't really built for that. It's a word soup generator, and not a technical database. For this kind of task, you probably want a model tha
by mystified5016 2y ago
Probably because it isn't really built for that. It's a word soup generator, and not a technical database.
For this kind of task, you probably want a model that has specifically been trained on every product datasheet ever, and not ten million reddit threads and forum posts about how a 555 or 328p can solve any problem.
I doubt that chatgpt has been fed every datasheet for every part made in the last decade or two. Even if it had, that's likely far outweighed by the amout of noise coming from people talking about the most common parts.
But fundamentally I'm not sure that LLMs are great for this type of work. No two datasheets are the same and I've never seen one that wasn't missing some kind of information. What you very much do not want is an LLM hallucinating a value that does not actually exist in the datasheet. Or have it conflate two parts and mix up their values. These models just don't seem to be up to the task of returning real information from abstract queries. They're just meant to generate probabilistic text sequences.
- rasz 2y agoI would expect someone like Supplyframe to cook a custom model for that purpose. I think I remember them mentioning something about using ML to clean up old badly scanned/faxed datasheets a long time ago.
- journal 2y agoAre humans not word generators with arms and legs?
- bbourn 2y agoHey, don't tell everyone our secret sauce! ;) But yeah, you hit it on the head. LLMs alone were not enough to be able to read the datasheet. It took a mix of different algorithms, including OCR, Computer Vision, Neural Nets, and yes, LLMs to be able to consistently read all the differences between different manufactures, categories, etc. Give us a whirl at www.zenode.ai and let me know what you think, we just launched our MVP and while there's a ton to improve we think it's pretty helpful!