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> GPT-3 already uses about twice as many parameters This isn't accurate. GPT-3 has 175B parameters. The human brain has ~175B cells (neurons, glia, etc.) The a
by soraki_soladead 4y ago
> GPT-3 already uses about twice as many parameters
This isn't accurate. GPT-3 has 175B parameters. The human brain has ~175B cells (neurons, glia, etc.) The analog to GPT-3's parameter count would be synapses, not neurons, where even conservative estimates put the human brain at several orders of magnitude larger. It's likely that >90% of the 175B could be pruned with little change in performance. That changes the synapse ratios since we know the brain is quite a bit sparser. In addition, the training dataset is likely broader than the majority of Internet users. Basically, its not an apples-to-apples comparison.
That said, I agree that simply scaling model and data is the naive approach.
- benreesman 4y agoOPT-6.7B is good, but not even close to GPT-3. If you can get GPT-like performance out of a 17B model, you should publish that.
- soraki_soladead 4y agoI’m referring to post-training pruning not smaller models. This is already well-studied but it’s not as useful as it could be on current hardware. (Deep learning currently works better with the extra parameters at training time). Retrieval models (again, lots of published examples: RETRO, etc.) that externalize their data will bring the sizes down by about that order as well.
- benreesman 4y agoI agree that RETRO is cool. I think you might be stretching it a bit with the applicability, but I take your point.