---
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).