Fred Zhang, Neel Nanda, John Mary
We lifted 6 functions out of this paper's own repositories and ran 5 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| redwoodresearch/Easy-Transformer | canonical | 5 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| cst_fn | Ran | redwoodresearch/Easy-Transformer/easy_transformer/experiments.py code served (permissive licence) · get_code("7cc9d58376c94a83") |
| get_corner | Ran | redwoodresearch/Easy-Transformer/easy_transformer/utils.py code served (permissive licence) · get_code("1f5a0cbe7a2e0eac") |
| get_sample_from_dataset | Ran | redwoodresearch/Easy-Transformer/easy_transformer/utils.py code served (permissive licence) · get_code("0f4a0aa12ed98682") |
| neg_fn | Ran | redwoodresearch/Easy-Transformer/easy_transformer/experiments.py code served (permissive licence) · get_code("d3b030e4dd0ab13d") |
| zero_fn | Ran | redwoodresearch/Easy-Transformer/easy_transformer/experiments.py code served (permissive licence) · get_code("5e871c5d5f468bff") |
| download_file_from_hf | Not yet run | redwoodresearch/Easy-Transformer/easy_transformer/utils.py code served (permissive licence) · get_code("b05aad9a321cedec") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Mechanistic interpretability seeks to understand the internal mechanisms of machine learning models, where localization-identifying the important model components-is a key step. Activation patching, also known as causal tracing or interchange intervention, is a standard technique for this task (Vig et al., 2020), but the literature contains many variants with little consensus on the choice of hyperparameters or methodology. In this work, we systematically examine the impact of methodological details in activation patching, including evaluation metrics and corruption methods. In several settings of localization and circuit discovery in language models, we find that varying these hyperparameters could lead to disparate interpretability results. Backed by empirical observations, we give conceptual arguments for why certain metrics or methods may be preferred. Finally, we provide recommendations for the best practices of activation patching going forwards.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2309.16042")
get_code_for_paper("2309.16042")
have("2309.16042")
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