alt Hacker NewsThis is interesting and applicable to so many domains where people want to query a model about a dataset, and have it summarise or otherwise analyse that dataset.
One of the biggest problems about LLMs as machines for analysis is that they don't respect the boundaries of a problem and instead generate base on their corpus and training as well as the actual dataset in question in the context.
It would be wonderful if we could easily strip out facts about Enron somehow from a model or just instruct it to ignore anything outside the dataset (and have the instruction respected), it is quite frustrating that these machines are so fuzzy.
I had a look at the methodology page, well done for doing the hard work of going over the results a few times to find errors/inconsistencies. There are not too many inconsistencies according to that.
Of 62,205 candidate facts, 37 quoted a sentence we couldn't find in the message
https://www.thisandthat.chat/enron/methodology.html
The difficulty for a business considering using this (in law for example or analysing internal business documents), is that one incorrect fact could be very damaging and it's very hard to check them all.
I completely agree. It's hard to imagine this ever being perfect both because of AI and because people make mistakes in communications all the time. In our own usage of it we've found it essential to check things that seem suspicious once they're in the company brain. But, the more important gate, and one we expect may remain longer than people would like, is that we ask people to approve changes to a company brain that are proposed. Even this isn't perfect but it is a key defense from the brain turning into slop. And it's different than this demo, which was much closer to fully automated.