| --- |
| license: mit |
| task_categories: [feature-extraction] |
| tags: [interpretability, linear-probes, llm, activations, ood, probe-stability] |
| pretty_name: ProbeShift Activation Cache |
| size_categories: [100B<n<1T] |
| --- |
| |
| # ProbeShift Activation Cache |
|
|
| Residual-stream activations backing the **ProbeShift** benchmark — a label-free study of |
| linear-probe **direction stability** under label-preserving semantic shift. Ships so the |
| benchmark's numbers reproduce in minutes (no re-extraction needed). |
|
|
| ## Layout |
| ``` |
| cache_seed{0..4}/<model>/<dataset>/<distribution>/ |
| acts.npy float16 [N, L+1, H] masked-mean-pooled residual stream (L+1 = embeddings + L layers) |
| labels.npy int64 [N] |
| ids.npy int64 [N] stable example ids (align across distributions) |
| meta.json {model, dataset, distribution, pooling, n, n_layers, hidden} |
| ``` |
|
|
| - **models** (8): pythia-70m/160m/410m/1.4b/6.9b, gpt2, gpt2-medium, qwen2.5-0.5b |
| - **datasets** (14 → 12 concepts): sst2, imdb (sentiment); ag_news, dbpedia (topic); counterfact (truth); |
| emotion; tweet_hate; tweet_irony; tweet_offensive; subj (subjectivity); spam; cola (grammaticality); |
| stance; amazon_cf (counterfactual) |
| - **distributions**: `train`, `iid`, `paraphrase`/`domain`/`length` (label-preserving OOD shifts), |
| `aug0`/`aug1`/`aug2` (de/fr/ru back-translation augmentations) |
| - **seeds**: 0–4 (each an independent example draw + independent paraphrase — Option A replication) |
| |
| ## Load |
| ```python |
| import numpy as np |
| acts = np.load("cache_seed0/pythia-410m/sst2/iid/acts.npy", mmap_mode="r") # [N, L+1, H] fp16 |
| labels = np.load("cache_seed0/pythia-410m/sst2/iid/labels.npy") |
| ``` |
| |
| Code & paper: see the ProbeShift repository. All activations produced on a single RTX 4090 |
| (≤200 GPU·h, $0 API, zero new annotation). |
| |
