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.

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.