Pretraines LVMs can do many things, they are a powerful tool in our toolbox. But they are limited to the tasks they were pretrained on, and may come with subpar accuracy at scale or unknown biases that raise PR red flags.
LVMs also require expensive hardware to run, they are slow, and can be expensive to fine-tune.
I've worked on prod vision classifications models that run on cheap CPUs and even raspberry pis. For large scale companies, the difference can be $10k+ vs < $10 monthly cloud bills.
The other thing to consider is that collecting data for supervised learning can be fairly cheap. $5k spend on manual labeling is cheap compared to an engineer, and more importantly that can become a strategic IP advantage (there's no moat around open source-LVM applications).
If we have a use-case that LVMs support, it can be a good way to get to market faster. Once proven, I would seriously look at using the LVM plus human review to build a dataset for supervised training a cheap/fast/simple model from scratch.
> It’s kind of like what we’ve seen in the NLP world. People aren’t training language models from the ground up anymore; they’re taking pre-trained models and fine-tuning them for their specific needs.
This is false. Everything I wrote applies to LLMs.
I did not know about "Betteridge's law of headlines", quite interesting. Thanks for sharing :)
You raise some interesting points.
1) Safety: It is true that LVMs and LLMs have unknown biases and could potentially create unsafe content. However, this is not necessarily unique to them, for example, Google had the same problem with their supervised learning model https://www.theverge.com/2018/1/12/16882408/google-racist-go.... It all depends on the original data. I believe we need systems on top of our models to ensure safety. It is also possible to restrict the output domain of our models (https://github.com/guidance-ai/guidance). Instead of allowing our LVMs to output any words, we could restrict it to only being able to answer "red, green, blue..." when giving the color of a car.
2) Cost: You are right right now LVMs are quite expensive to run. As you said are a great way to go to market faster but they cannot run on low-cost hardware for the moment. However, they could help with training those smaller models. Indeed, with see in the NLP domain that a lot of smaller models are trained on data created with GPT models. You can still distill the knowledge of your LVMs into a custom smaller model that can run on embedded devices. The advantage is that you can use your LVMs to generate data when it is scarce and use it as a fallback when your smaller device is uncertain of the answer.
3) Labelling data: I don't think labeling data is necessarily cheap. First, you have to collect the data, depending on the frequency of your events could take months of monitoring if you want to build a large-scale dataset. Lastly, not all labeling is necessarily cheap. I worked at a semiconductor company and labeled data was scarce as it required expert knowledge and could only be done by experienced employees. Indeed not all labelling can be done externally.
However, both approaches are indeed complementary and I think systems that will work the best will rely on both.
Thanks again for the thought-provoking discussion. I hope this answer some of the concerns you raised