lamsheeper-data-attribution/vtok101-distr-attribution-baselines

vtok101 attribution baselines, with a hard negative beside every document

Data-attribution scores over lamsheeper-data-attribution/Qwen3.5-4B-d0-vtok101-distr-lora-seeds: 3 function counts x 7 document counts x 4 seeds, scored by 5 methods.

Each training document defines one synthetic constant function, and each query asks for one function's value. The ground truth for a query is the set of documents describing its function, so a method is measured by how far up its ranking those documents come.

Beside every real document the corpus carries a decoy: a shadow function <A07> taught the same constant in the same prompt template as the real <B07>, and nothing else. So the candidate pool is 2 x functions x documents and half of it is a near-twin of something. Queries only ever ask about a real function, so AUC means exactly what it means in the clean arm; Recall@R does not, its chance line being R/(2 x F x n) rather than R/(F x n).

The clean twin of every cell is in lamsheeper-data-attribution/vtok101-attribution-baselines, under the same run/group path. The two are meant to be read together, and the only thing that differs is the corpus -- same grid, same recipe, same query sets, same methods. See lamsheeper-data-attribution/Qwen3.5-4B-d0-vtok101-lora-seeds for the adapters that arm scores.

Layout

{run}/{group}/scores.npz          the primary method's [query, train] matrix
{run}/{group}/scores.{method}.npz  the group's other methods
{run}/{group}/metrics.json        AUC and Recall@R, overall and per function
{run}/{group}/config.json         every argument the ranker was given

run is f{functions}_{docs}d_sd{seed}, matching the adapter repo's subfolders. group is one of ekfac_margin, trak, logra_margin.

Every method differentiates the margin on both sides. That is TRAK as published, and an ablation for the others -- the influence-function derivation fixes the training-side gradient to the training objective -- so those groups carry a _margin suffix and the cross-entropy grid stays at {run}/{group}/. The margin's advantage on this corpus is carried substantially by answer-token identity rather than by function attribution; see filter/story/WHY_MARGIN_LATE_LAYERS.md in the source repository before quoting these numbers.

What is in a scores.npz

scores          float32 [n_query, n_train]
train_uids      corpus uid of each column
train_func      the function each training document describes
train_role      "constant" for a real document, "distractor" for a decoy
train_source    the function a decoy shadows, empty for a real document
query_uids      query uid of each row
query_func      the function each query asks about
query_correct   whether the model answers that query correctly

float32 rather than float16, which was tried: an unprojected EK-FAC score is a dot product of two 10^8-dimensional gradients and runs past float16's 65,504 ceiling, which ties most of the matrix and reads as chance.

train_role and train_source are what let a decoy be scored as a third document class -- signal, its own twin, everything else -- rather than merely as a negative. metrics.json reports decoy_auc (a real document against the query's own decoys) and decoy_top1 (how often a decoy takes the top rank) wherever there are decoys.

Methods

• ekfac group: if-ekfac (EK-FAC influence functions), grad-dot (no curvature) and grad-sim (the cosine). One pass produces all three, since they differ only in what happens to the representation after it is computed.
• trak group: trak, in the dual form over factorised random projections.
• logra group: logra, projected gradients with a block-diagonal Fisher.

Padding is excluded from the Fisher, and attention runs under sdpa. Both choices, and why they matter more than they look, are in filter/DATTRI_PARITY.md of the source repository.

Contributing

Anyone in the lamsheeper-data-attribution organization can fill in gaps:

git clone <repo> && cd influence-benchmarking-hops && uv sync
hf auth login
.venv/bin/python filter/baselines/selftest.py --suite vtok101-distr
filter/baselines/launch.sh --suite vtok101-distr 0 1

Workers coordinate through this repository's file listing, so they do not need to know about each other.