Three small LLMs in a trenchcoat.
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Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM
31–40 of 49 posts
Re: Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM
#32Ok, this seems bunk basically because they never really provide evidence of "better". > ... traditiontal gold-standard approaches use human evaluators that score the quality of generated responses, which can be costly. However, since chat AIs are by definition deployed in social environments with humans, one can leverage statistics of users interaction as a meaningful and aligned measure of chat AI engagingness and q…
They're making chatbots specifically for humans to waste time with them (a.k.a. entertainment.) Engagement and user retention are directly connected to their bottom line in a way that quality responses (e.g. introducing you to a more fulfilling hobby than chatting with AIs) are not.
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Re: Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM
#33Earlier quoted context omitted.
They're making chatbots specifically for humans to waste time with them (a.k.a. entertainment.) Engagement and user retention are directly connected to their bottom line in a way that quality responses (e.g. introducing you to a more fulfilling hobby than chatting with AIs) are not.
That is what I read in this paper as well. It is not about "better as better performance" it is "better as improved user retention".
The papers title makes a stronger claim than the abstract. The abstract makes a stronger claim than the paper. It is like they couldn't decide what paper to write.
Edit: Thinking about it, this is exactly what you might expect from a paper written by a stochastic mix of experts.
Re: Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM
#34I've said this a few times previously yet I certainly want to say it again - "All You Need" titles are definetely not what all we need.
Re: Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM
#35I've said this a few times previously yet I certainly want to say it again - "All You Need" titles are definetely not what all we need.
What is especially egregious about the title is that "blending" requires stuff to blend. It's not all you need! It makes no sense at all.
Re: Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM
#36I’ve said this before but every time someone uses 3.5 to make a point, there’s an agenda.
Re: Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM
#37Earlier quoted context omitted.
They're making chatbots specifically for humans to waste time with them (a.k.a. entertainment.) Engagement and user retention are directly connected to their bottom line in a way that quality responses (e.g. introducing you to a more fulfilling hobby than chatting with AIs) are not.
That is what I read in this paper as well. It is not about "better as better performance" it is "better as improved user retention".
Optimizing AIs to be addictive to humans is always how humanity will end. It was the natural end to social media, and market forces will force the same to happen in this industry.
People worry about the robot uprising killing all humans but never think about the far more likely AI domestication of humans.
Re: Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM
#38machine learning papers are astrology for men
Re: Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM
#39Now that I think about it, doesn't this "technique" triple the amount of compute and memory per generated token since each model needs to also compute and store the KV values for the two previous tokens it didn't generate and thus has never seen? Edit: On second thought, depending on how it's actually implemented the other two tokens are probably ran through the model in parallel so it shouldn't be all that much slow…
It doesn’t generate three responses for every turn. It randomly picks a model for every response, the claim being that the switching between different models leads to better conversations because of the diversity of each model’s training.
Re: Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM
#40Foundational models are designed to be universally applicable, covering a wide range of use cases. While it's relatively easy to tailor smaller models to specific scenarios through overfitting, when a model is overly specialized, it loses its broad applicability and ceases to be a foundational model.