I agree that Gemini will give us an update on timelines. But even if it’s not particularly impressive, there’s another route to LLM improvements that should be mentioned in any discussion on LLM timelines.
The capabilities of LLMs can be easily and dramatically improved, at least in some domains, by using scaffolding scripts that prompt the LLM to do internal reasoning and call external tools, as in HuggingGPT. These include creating sensory simulations with generative networks, then interpeting those simulations to access modality-specific knowledge. SmartGPT and Tree of Thoughts show massive improvements in logical reasoning using simple prompt arrangements. Whether or not these expand to be full language model based cognitive architectures (LMCAs), LLMs don’t need to have sensory knowledge embedded to use it. Given the ease of fine-tuning, adding this knowledge in an automated way seems within reach as well.
I agree that Gemini will give us an update on timelines. But even if it’s not particularly impressive, there’s another route to LLM improvements that should be mentioned in any discussion on LLM timelines.
The capabilities of LLMs can be easily and dramatically improved, at least in some domains, by using scaffolding scripts that prompt the LLM to do internal reasoning and call external tools, as in HuggingGPT. These include creating sensory simulations with generative networks, then interpeting those simulations to access modality-specific knowledge. SmartGPT and Tree of Thoughts show massive improvements in logical reasoning using simple prompt arrangements. Whether or not these expand to be full language model based cognitive architectures (LMCAs), LLMs don’t need to have sensory knowledge embedded to use it. Given the ease of fine-tuning, adding this knowledge in an automated way seems within reach as well.
latent capacity overhang
Yes. That’s why we should include these likely improvements in our timelines.