[Kranar]

Being accurate is a feature and it is a bug that can be fixed though.

Given various models, one that always produces statements that are false and another that only sometimes produces false statements, the latter model is preferable and the model which most people intend to use, hence the degree to which a model produces correct statements is absolutely a feature.

And yes, it's absolutely possible to systematically produce models that make fewer and fewer incorrect statements.

[leptons]

It's nice that you feel that way, but reality is at odds with your sentiment. Even if the LLM is trained on completely 100% factual human-checked data, its mechanism is still predicting the next word, and what it is not is a mechanism designed to return only factual data. There is no such thing as an infallible LLM, no matter the model or how it was trained.

Sure, some may return results that are sometimes more true than others, but a broken clock is also right twice a day. The more broken clocks you have, the more chance there is that one of them is correct.

[AudiomaticApp]

No, the user you replied to is correct. Accuracy is indeed a feature, and can be incrementally improved. "Predicting the next word" is indeed a mechanism that can be improved to return increasingly accurate results.

Infallibility is not a feature of any system that operates in the real world. You're arguing against a strawman.

[norm_namillu]

surely next time we 10x the r&d budget will iron this out...

[Kranar]

It's nice that you feel that having one LLM that generates entirely incorrect statements is equally as functional as an LLM that does not, but reality in terms of what LLMs people will actually use in real life and not for the sake of being pedantic over an Internet argument is very much at odds with your sentiment.

How a product happens to currently be implemented using current machine learning techniques is not the same as the set of features that such a product offers and it's absolutely the case that actual researches in this field, those who are not quibbling on the Internet, do take this issue very seriously and devote a great deal of effort towards improving it because they actually care to implement possible solutions.

The feature set, what the product is intended to do based on the motivations of both those who created it and those who consume it, is a broader design/specification goal, independent of how it's technically built.

[leptons]

>LLM that generates entirely incorrect statements is equally as functional as an LLM that does not

And yet they would both be operating within the normal design parameters, even the supposed "LLM that does not" when it spits out nonsense every so often.

Your current zeitgeist is not much better than a broken clock, and that is the reality many people are witnessing. Whether or not they care if they are being fed wrong information is a whole other story entirely.