Natural Language Autoencoders: Turning Claude's Thoughts into Text
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Re: Natural Language Autoencoders: Turning Claude's Thoughts into Text
#2It will inevitably learn how to think in a way that translates to one (moral) meaning and back but has an ulterior meaning underneath.
Re: Natural Language Autoencoders: Turning Claude's Thoughts into Text
#3Beautiful idea, an autoencoder must represent everything without hiding if is to recover the original data closely. So it trains a model to verbalize embeddings well. This reveals what we want to know about the model (such as when it thinks it is being tested, or other hidden thoughts).
Re: Natural Language Autoencoders: Turning Claude's Thoughts into Text
#4finally a something interesting but this only makes me think that the last judgement is still in human hands to judge claude inner thoughts is correct or not
I mean who knows if those are really claude thoughts or claude just think that is his thoughts because humans wants it
Re: Natural Language Autoencoders: Turning Claude's Thoughts into Text
#5[deleted]
Re: Natural Language Autoencoders: Turning Claude's Thoughts into Text
#6Anthropic Research going from strength to strength in interpretability. Publicly releasing the code so other labs can benefit from it is also a great move - very values aligned, and improves the overall AI safety ecosystem.
Re: Natural Language Autoencoders: Turning Claude's Thoughts into Text
#7Anthropic has released open weight models for translating the activations of existing models, viz. Qwen 2.5 (7B), Gemma 3 (12B, 27B) and Llama 3.3 (70B) into natural language text. https://github.com/kitft/natural_language_autoencoders https://huggingface.co/collections/kitft/nla-models This is huge news and it's great to see Anthropic finally engage with the Hugging Face and open weights community!
Re: Natural Language Autoencoders: Turning Claude's Thoughts into Text
#8> 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.
Re: Natural Language Autoencoders: Turning Claude's Thoughts into Text
#9It will inevitably learn how to think in a way that translates to one (moral) meaning and back but has an ulterior meaning underneath.
This is exactly what I first thought. “The user appears to be attempting to decode my previous thought process, …”, the question is whether or not the model will be able to internalize this in such a way that is undetectable to the aforementioned technique.
Re: Natural Language Autoencoders: Turning Claude's Thoughts into Text
#10> 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.
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.