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Anish Mudide retweeted
🧵Can we understand vision language models by interpreting linear directions in their latents? Yes! In our new paper, Line of Sight, we use probing, steering, and SAEs as useful tools to interpret image representations within VLLMs.
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I'm at ICLR to present Switch SAEs. Come by 3pm - 5:30pm today at Hall 3 + Hall 2B #272.
I'm at ICLR to present Switch SAEs. Come by 3pm - 5:30pm today at Hall 3 + Hall 2B #272.
Anish Mudide retweeted
(1/11) New paper! “Low-rank adapting models for Sparse Autoencoders.” While SAEs find interpretable latents, they hurt downstream behavior—e.g. using TopK SAE activations on GPT-4 mimics a model trained w/ 10% compute. Our fix? Adapt the model for the SAE, not just vice versa.👇
the only thing worse than bad evals is realizing there are no bugs to blame
Our paper on Switch Sparse Autoencoders has been accepted to ICLR 2025 – see you in 🇸🇬!
Our paper on Switch Sparse Autoencoders has been accepted to ICLR 2025 – see you in 🇸🇬!
Anish Mudide retweeted
🦜Introducing the Stochastic Parrot 🦜: An AI-powered motivational companion! The Stochastic Parrot sits on your shoulder while it listens, looks, and talks to help you crush your goals 🎯(1/6)
Anish Mudide retweeted
Since the internal structure of neural networks, through training, comes to reflect the structure of the external world, advances in interpretability and our understanding of neural computation more generally could have a huge impact across science over the coming years...
Since the internal structure of neural networks, through training, comes to reflect the structure of the external world, advances in interpretability and our understanding of neural computation more generally could have a huge impact across science over the coming years...
Anish Mudide retweeted
Announcing Transluce, a nonprofit research lab building open source, scalable technology for understanding AI systems and steering them in the public interest. Read a letter from the co-founders Jacob Steinhardt and Sarah Schwettmann: transluce.org/introducing-tr…
Anish Mudide retweeted
1/11: New paper! "Decomposing the Dark Matter of Sparse Autoencoders." We find that SAE errors and error norms are linearly predictable using model activations. Why is this, and what does it imply for SAE scaling and the structure of language model representations? Answers in 🧵
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