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light24bulbs t1_jds3mdl wrote

What's the underlying approach here? Just prompt engineering right?

I really really want to apply the ToolFormer paper to llama. They're both Facebook systems, you can get they've done it.

ToolFormer just seems like SUCH a good and thorough approach. There are quite a few gaps between the paper and building a working example, IMO, but it's clearly doable.

The way Facebook licensed the weights is frustrating me. We should all be passing around Alpaca trained, GPTQ quantized, SparseGpt optimized Llama derived models by now. Is there some telegram group i need to be in or something?

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endless_sea_of_stars t1_jdskiit wrote

The advantage of in context learning is that it is trivial to add and remove plug-ins.

Training with the plug-ins is more powerful, but you can't really easily add or subtract. In theory training with APIs should result in a smaller model as the main model no longer needs to learn math or trivia (in theory).

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light24bulbs t1_jdsulyn wrote

By "in context learning" i take it you mean zero shot.

Yes, you can hot swap. Id be unsurprised if what Open-AI did is fine tune on how to use plugins in general by giving some examples combined with a little bit of zero-shot primer.

Something trained with ToolFormers technique and then told it can use a new, but similar, plugin is IMO going to generalize way better than something that's never used a plugin before.

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endless_sea_of_stars t1_jdtdiar wrote

Here is what we know about OpenAIs plug-ins. A compact API description gets prepended to the prompt. (In context) Technically it is few shot depending on which definitions you use. We don't know what if any fine-tuning of the model they did to get plug-ins working.

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light24bulbs t1_jdtgrjb wrote

Based on how much langchain struggles to use tools and gets confused on them, I'd bet on fine tuning. I asked a contact to reveal what they're injecting into the prompt but it's not public information yet so i couldn't get it

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endless_sea_of_stars t1_jdtik00 wrote

It is mostly public information. The API developer is required to make a specification document that describes the API. This gets injected into the prompt. They may transform it from json to something the model better understands. It may also inject some other boilerplate text.

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light24bulbs t1_jdtiq9w wrote

I'm aware of that part. The wording of the test that's injected is not public. If it was, if use it in my langchain scripts.

Again i really expect there's fine-tuning, we will see eventually maybe.

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alexmin93 t1_jdup63s wrote

Do you have GPT-4 API? Afaik plugins run on GPT-4 which even in current state is way better at following formal rules. But it's likely that they've indeed fine tuned it to make decisions to use tools

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