dspoka

Looks cool! Any advantages to the mini-lm model - it seems better on most mteb tasks but wondering if maybe inference or something is better.

Sensational title that misrepresents the message in paper.

However, when conducting more targeted automatic evaluations, we found that the imitation models close little to none of the large gap between LLaMA and ChatGPT. In particular, we demonstrate that imitation models improve on evaluation tasks that are heavily supported in the imitation training data. On the other hand, the models do not improve (or even decline in accuracy) on evaluation datasets for which there is little support. For example, training on 100k ChatGPT outputs from broad-coverage user inputs provides no benefits to Natural Questions accuracy (e.g., Figure 1, center), but training exclusively on ChatGPT responses for Natural-Questions-like queries drastically improves task accuracy.

Just because this might not be the way to replicate the performance of ChatGPT across all tasks, it seems to work quite well on whichever tasks are in the imitation learning. That is still a big win.

Later on this also works for factual correctness. (leaving aside the argument whether this is the right approach for factuality)

For example, training on 100k ChatGPT outputs from broad-coverage user inputs provides no benefits to Natural Questions accuracy (e.g., Figure 1, center), but training exclusively on ChatGPT responses for Natural-Questions-like queries drastically improves task accuracy.

Alpaca finetuned a llama so: https://news.ycombinator.com/item?id=35186185#:~:text=Tuned%....

My main qualm with the parent comment was this in particular, "Number one, is that forests work as long term carbon storage and sinks." Even with the surrounding context it sounded like this strategy will just "work".

The example you give with mangroves is a great one which does in fact work. Pragmatically and historically most of the attempts however, have not due to mismanagement and other unseen complications.

Seeing the further comments I see the point the parent was making is more around first principles of Forrests as carbon sinks not about its implementations.

And yet research on this topic suggest the opposite. "These days everyone seems to thinks that "planting trees" is an important solution to the climate crisis. They're mostly wrong, and in this paper we explain why. Instead of planting trees, we need to talk about people managing landscapes." [1]

[1] https://twitter.com/ForrestFleisch1/status/13062214459331297...

Do you have any good write up on this? Was always interested in the how this space is still locked up.

I think the reaction from software devs on how Copilot's uses their code for ML is interesting in that all the ML companies have been doing this with all other forms of produced content: texts, posts, messages, photo captions, etc. And most likely even less care went into adhering to laws or ethics. Yes code has licenses and thus more distinct legal ramifications but on the other side are people who don't really understand that every time they interact with software or produce some content, everything is gathered and harnessed to power all these companies.

Chief Medical Officer of Intel talking about what hacks look like on people's genetic information.

https://www.youtube.com/watch?v=HKQDSgBHPfY

Is sitting in a housing court just allowed? How does one go around doing this?

Great to see you working on this!

I was wondering if you could estimate what it would cost to have always on recording of all these radio conversations, cost of running this speech2text ML and cost of labeling this data.

I think having these rough estimates will make donations easier for people.

Something I came across for this : https://app.astralapp.com/

Is there some sort of pytorch 1.0 migration guide or does anyone know if there is any breaking from .41 to 1.0 ?

Do you have any information on pricing?