This actually isn’t a terrible use of an LLM. It’s actually kind of refreshing to see a news story about a beneficial use of it in a very specific context.
Could be a decent moderating tool too since increasing layers of Innuendo wouldn’t be as likely to dodge a pattern seaking algoriðm as ðey would be an underpayed overworked hand sorting mod.
Next, ban SFH HOAs.
This is exactly the kind of task I’d expect AI to be useful for; it goes through a massive amount of freshly digitized data and it scans for, and flags for human action (and/or) review, things that are specified by a human for the AI to identify in a large batch of data.
Basically AI doing data-processing drudge work that no human could ever hope to achieve with any level of speed approaching that at which the AI can do it.
Do I think the AI should be doing these tasks unsupervised? Absolutely not! But the fact of the matter is; the AIs are being supervised in this task by the human clerks who are, at least in theory, expected to read the deed over and make sure it makes some sort of legal sense and that it didn’t just cut out some harmless turn of phrase written into the covenant that actually has no racist meaning, intention or function. I’m assuming a lot of good faith here, but I’m guessing the human who is guiding the AI making these mass edits can just, by means of physicality, pull out the original document and see which language originally existed if it became an issue.
To be clear; I do think it’s a good thing that the law is mandating and making these kinds of edits to property covenants in general to bring them more in line with modern law.
Santa Clara County alone has 24 million property records, but the study team focused mostly on 5.2 million records from the period 1902 to 1980. The artificial intelligence model completed its review of those records in six days for $258, according to the Stanford study. A manual review would have taken five years at a cost of more than $1.4 million, the study estimated.
This is an awesome use of an LLM. Talk about the cost savings of automation, especially when the alternative was the reviews just not getting done.
Specialized LLMs trained for specific tasks can be immensely beneficial! I’m glad to see some of that happening instead of “Company XYZ is now needlessly adding AI to it’s products because buzzwords!”
Did you see something that said it was an LLM?
Edit: Indeed it’s an LLM. They published the model here: https://huggingface.co/reglab-rrc/mistral-rrc
Given the error rate of LLMs, it seems more like they wasted $258 and a week that could have been spent on a human review.
LLMs are bad for the uses they’ve been recently pushed for, yes. But this is legitimately a very good use of them. This is natural language processing, within a narrow scope with a specific intention. This is exactly what it can be good at. Even if does have a high false negative rate, that’s still thousands and thousands of true positive cases that were addressed quickly and cheaply, and that a human auditor no longer needs to touch.
What do you believe would make this particular use prone to errors?
The use of LLMs instead of someone that can actually understand context.
One of LLMs main strengths over traditional text analysis tools is the ability to “understand” context.
They are bad at generating factual responses. They are amazing at analysing text.
I think you may have misunderstood the purpose of this tool.
It doesn’t read the deeds, make a decision, and submit them for termination all on its own. It reads them, identifies racial covenants based on patterns of language (which is exactly what LLMs are very good at), and then flags them for a human to review.
This tool is not replacing jobs, because the whole point is that these reviews were never going to get the budget and manpower to be done manually, and instead would have simply remained on the books.
I get being disdainful or even angry about LLMs in our unregulated-capitalism anti-worker hellhole because of the way that most companies are using them, but tools aren’t themselves good or bad, they’re just tools. And using a tool to identify racial covenants in legal documents that otherwise would go un-remediated, seems like a pretty good use to me.
