| --- |
| license: mit |
| language: |
| - en |
| tags: |
| - influence-functions |
| - attribution |
| - magic |
| - bergson |
| - gpt-2 |
| - wikitext |
| --- |
| |
| # bergson MAGIC scores — GPT-2 / wikitext-2 / random validation |
|
|
| Per-document MAGIC attribution scores for a GPT-2 model fine-tuned on |
| `Salesforce/wikitext` `wikitext-2-raw-v1` `train`, attributing the |
| training-loss gradient with respect to the model's output on |
| `test[3:4]` (a single test example, taken as the query). |
|
|
| Scores were produced by the [`bergson`](https://github.com/EleutherAI/bergson) |
| attribution toolkit's MAGIC subcommand, which back-propagates through |
| the entire training trajectory to compute, for each training doc `d`, |
| `∂(L_query)/∂(w_d)` — the first-order Taylor expansion of how much |
| removing doc `d` from training would change the query loss. |
|
|
| ## Files |
|
|
| | file | description | |
| |---|---| |
| | `scores.pt` | torch.float32 tensor, shape `(36718,)`. Indexed by original wikitext-2 train row position (filtered <2-token rows have score 0). | |
| | `summary.csv` | Final Spearman/Pearson correlation between MAGIC scores and 100-subset leave-k-out training-loss diffs at validate time. | |
| | `validation.csv` | Per-subset (`subset, diff, score_sum`) records from the leave-k-out validation. | |
|
|
| ## Loading |
|
|
| ```python |
| import torch |
| scores = torch.load("scores.pt", map_location="cpu", weights_only=True) |
| # scores.shape == (36718,), one entry per original wikitext-2 train row. |
| ``` |
|
|
| ## Validation result |
|
|
| Random-strategy 100-subset leave-k-out validation, batch_size=256, |
| num_epochs=2, polynomial LR schedule: |
|
|
| | metric | value | p | |
| |---|---|---| |
| | Spearman ρ | **+0.9731** | 2.71e-64 | |
| | Pearson r | **+0.9724** | 1.00e-63 | |
| | baseline_loss | 3.6840 | | |
| |
| (Sorted-strategy validation against the same scores reaches ρ = +0.9926 |
| on the same 100 subsets.) |
| |
| ## YAML used to generate these scores |
| |
| ```yaml |
| run_path: runs/gpt2_wikitext |
| model: gpt2 |
| overwrite: true |
| |
| data: |
| dataset: Salesforce/wikitext |
| subset: wikitext-2-raw-v1 |
| split: "train" |
| chunk_length: 512 |
|
|
| query: |
| dataset: Salesforce/wikitext |
| subset: wikitext-2-raw-v1 |
| split: "test[3:4]" |
| chunk_length: 0 |
| |
| distributed: |
| nproc_per_node: 4 |
| nnode: 4 |
| |
| batch_size: 256 |
| num_epochs: 2 |
| lr_schedule: |
| lr_scheduler_type: polynomial |
| lr: 0.0008 |
| lr_start: 1e-6 |
| lr_end: 0.00008 |
| warmup_steps: 0.25 |
| |
| subset_strategy: random |
| wandb_project: magic |
| ``` |
| |
| Saved as `examples/magic/gpt2_wikitext.yaml` in the bergson repo. |
|
|
| Run with: |
|
|
| ```bash |
| bergson magic examples/magic/gpt2_wikitext.yaml |
| ``` |
|
|
