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0 pointsw1nst0nsm1th3y ago0 comments

I asked it what was the xbrl taxinomy tag on us gaap for change in executive management of a SEC registered company in SEC filling and the answer doesn't fit compared to the whole xbrl taxinomy published on the SEC website. It also answered me 2 different kind of SEC form for it. It also gave me the correct url for the us gaap xbrl taxinomy on sec website.

That being said, both xbrl.org and the SEC document for us gaap xbrl reporting (an xml document) are kind of greedy about providing a documentation for what the tags actually cover. xbrl.org provide no documentation at all and advise an xbrl.org membership for developers, And the SEC document provided the tags but no information of what the tags cover.

The answer from chatGPT seems to about 'labels', used in xbrl document to describe xbrl taxinomy tag in different contexts, for example 'income in miami store'. But a change in a top executive position, like for 'CFO', once again required in SEC filling, shouldn't be subject to various arbitrary kind of label, because then the whole thing make no sense. If you call a 'cat', a 'little domestic pet'...

I searched google for the tag or label provided by chatGPT and google provided zilch. I searched the document provided by the SEC website, zilch again.

So either the code for the SEC form is wrong, either the tag or the label is wrong... or I don't know what else.

It seems, according to comments and posts from HN, that chatGPT can give good approximative answer, but fails without any notice once you ask for details.

According to an article published on HN a few days ago, 'chatGPT hallucinate facts'.

0 comments

ben_w3y ago

It absolutely does indeed hallucinate[0] on occasion.

Despite how remarkable and useful it already is, don't make the mistake of putting it unsupervised in charge of anything, as it's going to mess up at least as often as a self driving car.

[0] or whatever we want to call the behaviour; also seen it called BSing (because it doesn't really know what truth is) and "mansplaining as a service"

w1nst0nsm1thOP3y ago

It seems accurate for domains where it has large dataset (major programming langages like python, html, ...) to build model from.

xbrl is probably not the case as it is a very specialized domain, that is business reporting in standardized electronic format, according to specific local accounting standard, for example US GAAP in the us.

Only banks, (possibly) investment funds and accounting department in publicly traded companies, and financial regulation organisations (at least in the US) have invested that field.

This explains may be that.

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