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Found 4 papers, 3 papers with code
DeepDecipher: Accessing and Investigating Neuron Activation in Large Language Models
By granting access to state-of-the-art interpretability methods, DeepDecipher makes LLMs more transparent, trustworthy, and safe.
Neuron to Graph: Interpreting Language Model Neurons at Scale
Conventional methods require examination of examples with strong neuron activation and manual identification of patterns to decipher the concepts a neuron responds to.
Detecting Edit Failures In Large Language Models: An Improved Specificity Benchmark
We use this improved benchmark to evaluate recent model editing techniques and find that they suffer from low specificity.
N2G: A Scalable Approach for Quantifying Interpretable Neuron Representations in Large Language Models
Understanding the function of individual neurons within language models is essential for mechanistic interpretability research.
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