Averaging model weights seems to help across textual domains as well, see Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Models and Scaling Expert Language Models with Unsupervised Domain Discovery. I wonder if the two types of averaging (across hyperparameters and across domains) can be combined to produce even better models.

That’s appreciated!

Research into efficient optimization techniques seems pretty important given the scale of LLMs these days. Nice to see a second-order approach that achieves reasonable wall-clock improvements.

If there isn’t any discussion on reddit (no discussion in this case), I don’t see a reason to link to reddit; you can just link to the project page. That said, if you think there is important discussion happening that is helpful for understanding the paper, then use a teddit link instead, like:

https://teddit.net/r/MachineLearning/comments/14pq5mq/r_hardwiring_vit_patch_selectivity_into_cnns/

Please don’t post links to reddit.

It seems like for creative text generation tasks, metrics have been shown to be deficient; this even holds for the new model-based metrics. That leaves human evaluation (both intrinsic and extrinsic) as the gold standard for those types of tasks. I wonder if the results from this paper (and other future papers that look automatic CV metrics) will lead reviewers to demand more human evaluation in CV tasks like they do for certain NLP tasks.

hmmm… not sure which model you’re referring to. do you have a paper link?

do you have a link?

@Koffindodjer indeed you are!

If the effect is strong enough, then it could have a very negative effect on LLM training in the near future, considering more and more of the internet contains ChatGPT & GPT-4 content in it and automatic detectors are currently quite poor.