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.
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.
didn’t just cut out some harmless turn of phrase written into the covenant that actually has no racist meaning
I gotta say, because of the nature of systemic racism turns of phrase that are ambiguous or are explicitly neutral can be prejudiced or discriminatory is different ways.
We can’t rely on a statistical model to tell us what is infringing on right. We have to be critical.
Next, ban SFH HOAs.
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.
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.
So, what? They’re going to pay a human to OK the output and the whole lot of them never even gets seen?
Say 12 minutes per covenant, that’s 1 million work hours that humans could get paid for. Pay them $50 an hour and it’s $50 million. That’s nothing, less than 36 hours worth of the $12.5 Billion in weapons shipments we’ve sent to Israel in the last year. We could pay for projects like this with the rounding errors on the budget for blowing up foreign kids, and the people we pay to do it could afford to put their kids through college.
Instead, we get a project to train a robotic bigotry filter for real estate legalese and 50 more cruise missiles from the savings.
I think you are confused about the delineation between local and federal governments. It’s not all one giant pool of tax money. None of Santa Clara County’s budget goes to missiles.
Also, this feels like you are too capitalism-pilled, and rather than just spending the $240 to do this work, and using the remaining $49,999,760 to just fund free college or UBI programs, you’re like, “how about we pay these people to do the most mind-numbingly, soul-crushingly boring work there is, reading old legal documents?”
You know what would actually happen if you did that? People would seriously read through them for 1 day, and then they’d be like, “clear”, “clear”, “clear” without looking at half of them. It’s not like you’re gonna find and fund another group to review the first group’s work, after all. So you’d still be where we are now, but you also wasted x* peoples’ time that they could have been enjoying doing literally anything else.
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.
LLMs neither understand nor analyze text. They are statistical models of the text they were trained on. A map of language.
And, like any map, they should not be confused for the territory they represent.
If you admit that they have issues with facts, why would you assume that the randomly generated facts their “analysis” produces must be accurate?
I mean they literally do analyze text. They’re great at it. Give it some text and it will analyze it really well. I do it with code at work all the time.
Because they are two completely different tasks. Asking them to recall information from their training is a very bad use. Asking them to analyze information passed into them is what they are great at.
Give it a sample of code and it will very accurately analyse and explain it. Ask it to generate code and the results are wildly varied in accuracy.
I’m not assuming anything you can literally go and use one right now and see.
The person you’re replying to is correct though. They do not understand, they do not analyse. They generate (roughly) the most statistically likely answer to your prompt, which may very well end up being text representing an accurate analysis. They might even be incredibly reliable at doing so. But this person is just pushing back against the idea of these models actually understanding or analysing. Its slightly pedantic, sure, but its important to distinguish in the world of machine intelligence.
