congrats！

Source: t.co/mV27ZZg5MN

Yea… he does have problems portraying female in stereotypical ways, big critics in China too

During the QA session, one stood up to her regarding this issue really respectfully and her response was: “That was not based on my judgment. That was based on the student's quote saying that the school was not teaching it, which meant that it applied to a lot of people from there."

Most of the talk discussed about bad practices. But only one slide mentioned specific group of people.

Haha which one has more nowadays?

Haha transformers really transformed both.

However, I feel like the division is even further… currently, seems like RL is taking over LM post training and many NLProc are dealing with language model enabled new applications

Thanks!

Imagine a world where these will be positively correlated

Quite possible!

Here, we found some early evidence that SAE features trained on language models are still meaningful to LLaVA.

More details will be provided in the post, and more details will be provided soon!

@JackGallifant

@oldbayes.bsky.social

@daniellebitterman.bsky.social

More on future potential reliance on LLM agent doing reviews and audits

I’m terrified by the massive openreview data. Potentially gonna bite back on us 🥲😥

END/🧵 Thanks to all our awesome co-authors:
@jannahastings.bsky.social

@daniellebitterman.bsky.social

And all our awesome collaborators who are not on the right platform yet! 🦋

Happy Thanksgiving! 🍂

5/🧵 Dive deeper into our methods, findings, and the implications of our research by checking out the full 📜 paper here: arxiv.org/abs/2405.05506
All our data can be downloaded from our website: crosscare.net

4.5/🧵 For the arxiv pretraining dataset, we also have an overall trend based on entity mentions! Guess which two terms are the big bump there back in 2019