Re:What Meta learned from Galactica, the doomed model
by RandomLensman on Friday November 17, 2023 @04:21AM
(#38301276)
Grounding in facts is the one thing where machines could actually improve upon humans (in the same way a calculator doesn't fail on every 10th calculation).
Re:What Meta learned from Galactica, the doomed model
by Lord-Jobo on Friday November 17, 2023 @08:57AM
(#38303588)
In the context of LLMs it's only ever going to work out that wath math/stats, because garbage in garbage out will taint LLM output roughly to the same degree as human generation. Because humans are the garbage producers in both scenarios.
To bypass the garbage in problem, you'll need a different AI structure from LLMs almost certainly, a proper logic model.
Re:What Meta learned from Galactica, the doomed model
by anonymouskimmer on Friday November 17, 2023 @12:09PM
(#38306456)
Is it the fundamental architecture of an LLM that makes this problematic, or the input?
Based on how it's reported that they work, I assume that even without garbage in their statistical reasoning of the next word would occasionally come up with bad output. E.g. they might come up with 2 + 2 = 5 because of mathematical inputs such as "1 + 2 + 2 = 5".
Re:What Meta learned from Galactica, the doomed model
by TeMPOraL on Saturday November 18, 2023 @06:41PM
(#38326187)
Except it's unlikely because they have a bit larger context and are modeling a bit more than one-dimensional probabilities of "what comes after this?".
I'd say that LLMs are more resistant to GIGO, as long as the fraction of garbage in training data is small - it'll look like outliers to the larger model.
I feel the "grounding in facts" was a big mistake, and to a large extent still is. The worst case of it was symbolic linguistic and knowledge graphs. Why? Because symbols don't exist, facts and concepts and words are fuzzy boundaries, their meaning defined mostly or entirely through associations with other words. We don't learn symbolically, we don't have strong "grounding in facts" - not at the language layer.
I'd rather say that language and understanding, the way we do it, is statistical in nature, and "grounding in facts" is done through feedback.