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The statement was "AI frenzy almost convinced me that sleep was the training of our neural network with all the prompts of the day." Prompts are specific to LL

The statement was "AI frenzy almost convinced me that sleep was the training of our neural network with all the prompts of the day." Prompts are specific to LLMs. Most neural networks don't have prompts. Additionally, prompts happen during LLM inference, not LLM training. There are many non-technical people who claim they have experience "training" LLMs, when they are just an end user who added a lot of tokens to the context window during inference.

sva_ 1y ago
> Additionally, prompts happen during LLM inference, not LLM training. It is pretty common during the fine-tuning phase.
- gitremote 1y ago
  Sure. Foundation models aren't fine-tuned, and companies fine-tune foundation models to optimize user experience. So they are modeling the animal brain on an even more specific type of LLM that happens to be related to being a consumer of AI products.
immibis 1y ago
You're being pretty pedantic about the specific term used. Everything they said makes sense if you change "prompts" to "training examples" and you wouldn't expect someone who hasn't implemented an AI model to know the difference. It's like someone said while driving the car "let's give it some gas" and you said "but the tank is almost full" when they obviously meant "let's press the accelerator pedal"
- lolive 1y ago
  Funnily I am interested in this semantic argument. Do LLM trainers actually feed their « beast » with prompts from the past? Especially ones that are human corrections upon false assumptions hallucinated by the LLM? As a non-specialist I would definitely see a lot of value in doing so, but I let you, experts, clarify that point.
dragonwriter 1y ago
> There are many non-technical people who claim they have experience "training" LLMs, when they are just an end user who added a lot of tokens to the context window during inference. Since in-context learning is a thing, “adding tokens to the context window”, at least with the intent and effect of having a particular impact on capabilities when inference is run on the context to which they were added, is, arguably, a kind of training.

sva_ 1y ago

> Additionally, prompts happen during LLM inference, not LLM training. It is pretty common during the fine-tuning phase.

gitremote 1y ago
Sure. Foundation models aren't fine-tuned, and companies fine-tune foundation models to optimize user experience. So they are modeling the animal brain on an even more specific type of LLM that happens to be related to being a consumer of AI products.

immibis 1y ago

You're being pretty pedantic about the specific term used. Everything they said makes sense if you change "prompts" to "training examples" and you wouldn't expect someone who hasn't implemented an AI model to know the difference. It's like someone said while driving the car "let's give it some gas" and you said "but the tank is almost full" when they obviously meant "let's press the accelerator pedal"

lolive 1y ago
Funnily I am interested in this semantic argument. Do LLM trainers actually feed their « beast » with prompts from the past? Especially ones that are human corrections upon false assumptions hallucinated by the LLM? As a non-specialist I would definitely see a lot of value in doing so, but I let you, experts, clarify that point.

dragonwriter 1y ago

> There are many non-technical people who claim they have experience "training" LLMs, when they are just an end user who added a lot of tokens to the context window during inference. Since in-context learning is a thing, “adding tokens to the context window”, at least with the intent and effect of having a particular impact on capabilities when inference is run on the context to which they were added, is, arguably, a kind of training.