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
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
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
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:
bergson magic examples/magic/gpt2_wikitext.yaml