The point here is: the justifications from AI engineers for why counting vs math aren't the same task, while valid, are irrelevant because marketing never brings up the limitation in the first place. So any logical person who doesn't know a lot about AI will arrive at a logical, albeit practically incorrect conclusion.

My hope from this article is to help non-AI experts figure out when they need to design around a flaw versus believe what's marketed.

And if I can't trust a so-called SOTA model to partially answer - say, recall each mention of the word "wizard" instead of just giving me the wrong answer - then why should I trust it to list out specific scenes? That's even harder to benchmark.

So you tell me: if a regular developer reads the above, how can they surmise that the model which can do higher-order math can't count?

This is meant to be for the developer who doesn't fit the above profile and thinks a model that has a million token context window and "can handle complex analysis, longer tasks with multiple steps, and higher-order math and coding tasks" (direct quote from Anthropic's website), actually can do those things.

The structured output element is really important too - subject for another post though!

Not everything needs to be entertaining to be useful.

The agentic approach falls apart because again, a self-querying mechanism or a multi-agent framework still needs to know where in the document to look for each subset of information. That's why I argue that you need an ontology. And at that point, agents are moot. A small 7b model with a simple prompt suffices, without any of the unreliability of agents. I suggest trying agents on an actually serious document, the problems are pretty evident. That said, I do hope that they get there one day because it will be cool.

Comments URL: https://news.ycombinator.com/item?id=40134392

Points: 65

# Comments: 61