Anthropic released some documents earlier this year due to the book copyright litigation. I think a few of those documents might be of interest to some people here and haven't gotten much attention. In this post I link to some of those documents, and quote the bits I find interesting.[1]...
Anthropic amended their certificate of incorporation a few weeks ago, because they plan on doing an IPO soon. I think they'll probably amend it again immediately before they close the IPO, and that amendment will probably be a lot more interesting than the most recent one (i.e. that one would...
TLDR: We train a “matryoshka” NLA that, unlike standard NLAs, is trained to put the most important details at the start; it is trained by randomly truncating the verbalizer’s explanations before showing them to the reconstructor. We find that our matryoshka NLA frontloads claims that are important to reconstruction (more...
Natural language autoencoders are a really cool mostly-unsupervised method for producing free-form text explanations of LLM activations. You should read that paper (or the blog post) about them before reading this. I trained[1] several Qwen3-8B NLAs with different length penalties: during RL, I subtracted the token count multiplied by the...
I used the Gemma 3 12B activation verbalizer (maps activations to English) and reconstructor (maps English to activations) described in the Natural Language Autoencoders (NLA) paper to generate a bunch of explanations for 20k random tokens from a pretraining dataset (Common Pile derivative) and another 20k random tokens from a...
Epistemic status: I think the main point of this post is probably (~80%) false, and there are probably more counterpoints I haven't thought of. I wrote the rest of the post as if my claims are true for ease of reading. I would appreciate it if you told me where...