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0 pointsjll291y ago0 comments

There is room for more than one use case and large language model type.

I predict there will be a zoo (more precisely tree, as in "family tree") of models and derived models for particular application purposes, and there will be continued development of enhanced "universal"/foundational models as well. Some will focus on minimizing memory, others on minimizing pre-training or fine-tuning energy consumption, some need high accuracy, others hard realtime speed, yet others multimodality like GPT4.o, some multilinguality, and so on.

Previous language models that encoded dictionaries for spellcheckers etc. never got standardized (for instance, compare aspell dictionaries to the ones from LibreOffice to the language model inside CMU PocketSphinx) so that you could use them across applications or operating systems. As these models are becoming more common, it would be interesting to see this aspect improve this time around.

https://www.rev.com/blog/resources/the-5-best-open-source-sp...

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CooCooCaCha1y ago

I disagree, transfer learning and generalization are hugely powerful and specialized models won't be as good because their limited scope limits their ability to generalize and transfer knowledge from one domain to another.

I think people who emphasis specialized models are operating under a false assumption that by focusing the model it'll be able to go deeper in that domain. However, the opposite seems to be true.

Granted, specialized models like AlphaFold are superior in their domain but I think that'll be less true as models become more capable at general learning.

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