Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Optimal ablation for interpretability
Authors: Maximilian Li, Lucas Janson
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 45,927 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,589 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then show that using OA produces meaningful improvements for several common downstream applications of measuring component importance. In section 3, we apply OA to algorithmic circuit discovery (Conmy et al., 2023)... In section 4, we use OA to locate relevant components for factual recall (Meng et al., 2022)... In section 5, we apply OA to latent prediction (Belrose et al., 2023a)... |
| Researcher Affiliation | Academia | Maximilian Li Harvard University Lucas Janson Harvard University |
| Pseudocode | Yes | Algorithm 1 Uniform gradient sampling |
| Open Source Code | Yes | All code can be found at https://github.com/maxtli/optimalablation. |
| Open Datasets | Yes | The Indirect Object Identification (IOI) subtask (Wang et al., 2022)... |
| Dataset Splits | No | We train OAT on 60% of the dataset and evaluate both methods on the other 40%. |
| Hardware Specification | Yes | All experiments were run on a single Nvidia A100 GPU with 80GB VRAM. |
| Software Dependencies | No | The paper does not provide specific software dependency versions (e.g., Python 3.x, PyTorch 1.y, CUDA z.w). |
| Experiment Setup | Yes | We use learning rates between 0.01 and 0.15 for the sampling parameters. We use a learning rate of 0.002 for a... |