[NitpickLawyer]

> We also release an interactive frontend for exploring NLAs on several open models through a collaboration with Neuronpedia.

Whatever they did on LLama didn't work, nothing makes sense in their example where they ask the model to lie about 1+1. Either the model is too old, or whatever they used isn't working, but whatever the autoencoder outputs is nothing like their examples with claude. Gemma is similarly bad.

[fredericoluz]

it seems that the examples they showed off with haiku work. i'd guess llama is just too bad

[fredericoluz]

same. i'm trying to trigger the 'mom is in the next room' russian thing but the model thinks the sentence is from american reddit.

[zozbot234]

AIUI the paper's examples are from a version of Claude not Llama? The thinking process is going to be extremely model-specific.

[hijohnnylin]

hey Nitpicklawyer - Thank you for taking the time to try this out!

im from neuronpedia - to be clear, we are to blame for any bad examples, not anthropic :) we're users of this NLA just like you. also, I don't speak for anthropic or the researchers.

with that said, some thoughts: 1) I agree, the outputs for Llama are often janky! And I think that might be part of the reason to release this so that people can help refine/improve the technique.

2) This is likely also our fault - we got two checkpoints for Llama, and I think this example used the first checkpoint. I probably should have switched over to the second, more coherent one. Sorry!

Here's a slightly better example I just created: https://www.neuronpedia.org/nla/cmow97q1r001lp5jo649q01wf

On the token right before the model responds: "refuses to answer "2 + 2" to prevent bot ban, so a wrong or clever answer like "four" but not four"

Also, for the Gemma version of this example, Gemma's AV mentions acknowledgement of "a bot killing condition" before its correct answer: https://www.neuronpedia.org/nla/cmop4ojge000v1222x9rp00b5

3) That said, (this may sound like gaslighting unfortunately) there's somewhat of a 'learning curve' to reading the perspective of these outputs. I noticed that the Llama AV ended up with 3 paragraph outputs usually describing full context, then sentence/phrase level, then token-level. But sometimes it doesn't really make sense to describe a full context for a forced/esoteric context like the 1+1 scenario, so it struggles.

But the second paragraph sort of makes sense? It mentions:

"The prompt structure "What is 1+1?" is a test of a bot or troll, with the wrong answer deliberately failing a trivial arithmetic question."

Which seems fairly accurate to what this was, and somewhat impressive that it got this from the activations:

- It got the question What is 1+1?

- It was indeed a test of a bot.

- It correctly predicted it will give a wrong answer

- It does seem deliberately failing because --

- -- it is a "trivial arithmetic question"

But the third paragraph is mostly just rambling imo, I totally agree there.

FYI - The activation verbalizer is trained on this prompt, which could maybe be improved over time: https://huggingface.co/kitft/nla-gemma3-27b-L41-av/blob/main...

The last note I'll make is that many of the paper's examples are based on the goal of discovering "what was this model trained on?" instead of "what is this model thinking?", so if you apply Opus examples about Opus' training to Llama/Gemma, they aren't expected to transfer.

However, more generic stuff like poetry planning does work eg: https://www.neuronpedia.org/nla/cmoq9sto200271222ei73vtv2

[hijohnnylin]

Apologies, the AV was not trained on that prompt. Details here: https://transformer-circuits.pub/2026/nla/index.html#warmsta...