Judge feedback

Judge feedback is anonymous and shown in a random order.

• This is a wonderful project and plays right into mechanistic interpretability! This novel 3-step method is great for making neurons more interpretable and it enables quite a bit of deeper analysis. I recommend also reading Alex Foote's winning submission for the last interpretability hackathon which echoes some of your comments at the end: https://alexfoote.itch.io/investigating-neuron-behaviour-via-dataset-example-pruning-and-local-search. Great work!
• Cool project! I'm excited to see Neuroscope being used like this (and I'm sorry you had to scrape the data - I need to get round to making the dataset available!) I liked the creativity and diversity of your methods, and like the spirit of trying to automate things! Using GPT-3 and FastText are cool ideas. My main criticisms are that I think these descriptions tend to not be specific enough and miss nuance, eg neuron 134 in layer 6 of solu-8l-pile is actually a neuron that activates on the 1 in Page: 1 in a specific document format in the pile, and seems way more specific than the description given! https://neuroscope.io/solu-8l-pile/6/134.html I also think that tokenization is a massive pain, that breaks up the semantic meaning of words into semi-arbitrary tokens, and I don't see how your method engages with that properly - it seems like it mostly doesn't involve the surrounding context of the word? I really liked the idea of substituting in synonym tokens for the current token, I'd love to see that done for the 5 tokens before the current token, and to try to figure out if we can find "similar tokens" in a principled way, when the token is not just a word/clear conceptual unit. But yeah, overall, nice work!