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The article is a bit general to discuss but there is one specific point it touches that is crucial: it was trained on a wide array of resources, with both "true

The article is a bit general to discuss but there is one specific point it touches that is crucial: it was trained on a wide array of resources, with both "true" (=corresponding to reality) content such as academic textbooks as well as tons of trash mixed with truth such as Twitter & Reddit posts and so on. An example: I asked it for the way to move an AWS account between two OUs in Terraform. It promptly generated the code including a reference to the "aws_organizations_move_account" resource. Since it looked suspicious to me, I checked it and the only place it exists is a Github Issues page where the code is a proposal for something that could work somewhere in the future (it doesn't, you move accounts in a different way). So if the people who trained the model decided that instead of making it general and versatile they will only focus on higher quality content, and made a manual selection if titles (most academic books, especially from respected publishers; public source code only from prominent projects with and adequately high number of developers and so on), they could produce a model that would be less eloquent but could generate less bullshit.

evandrofisico 3y ago
From my limited understanding, a smaller, more specialized model could produce more accurate responses, but the main thesis of the article would still be true. Such LLMs would still be statistic prediction models, with no concept of reality. We already know that specialist systems, such as those tried in the 80's are not very good in modeling reality, either. The thing is, using LLMs as knowledge bases or as a sort of modern oracles seems to me like using a powerful tool for something they are horrible at. They are good enough (but not perfect) text summarizers, text translators and text transformers, but are far from the so expected "AGI" some seem to be expecting them to be.

evandrofisico 3y ago

From my limited understanding, a smaller, more specialized model could produce more accurate responses, but the main thesis of the article would still be true. Such LLMs would still be statistic prediction models, with no concept of reality. We already know that specialist systems, such as those tried in the 80's are not very good in modeling reality, either. The thing is, using LLMs as knowledge bases or as a sort of modern oracles seems to me like using a powerful tool for something they are horrible at. They are good enough (but not perfect) text summarizers, text translators and text transformers, but are far from the so expected "AGI" some seem to be expecting them to be.