The ‘Finding Neurons in a Haystack’ Initiative at MIT, Harvard, and Northeastern University Employs Sparse Probing

It is common to think of neural networks as adaptable “feature extractors” that learn by progressively refining appropriate representations from initial raw inputs. So, the question arises: what characteristics are being represented, and in what way? To better understand how high-level, human-interpretable features are described in the neuronal activations of LLMs, a research team from […]

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