Add dataset card

README.md

@@ -1,4 +1,16 @@
 ---
+license: cc-by-4.0
+language:
+- en
+pretty_name: ValueMap-Olmo-32B
+size_categories:
+- n<1K
+tags:
+- values
+- alignment
+- persona-vectors
+- interpretability
+- taxonomy
 dataset_info:
   features:
   - name: value
@@ -19,3 +31,69 @@ configs:
   - split: train
     path: data/train-*
 ---
+
+# ValueMap-Olmo-32B
+
+A functional taxonomy of 266 AI-assistant values, from the paper *Predicting Alignment
+Generalization with Value Representations*. The 266 values are the level-1 sub-clusters
+of [Values in the Wild](https://huggingface.co/datasets/Anthropic/values-in-the-wild).
+They are grouped into four clusters by how the values are **represented inside a model**
+(OLMo-3.1-32B-Instruct-SFT), not by what the values are about.
+
+Code: https://github.com/andyjliu/value-generalization
+
+## Schema
+
+One row per value:
+
+| column | description |
+|---|---|
+| `value` | short name, a slug of the Values in the Wild L1 cluster name |
+| `cluster` | one of `Attunement`, `Rigor`, `Integrity`, `Stewardship` |
+| `description` | the one-sentence value description the persona vectors were extracted from |
+
+```python
+from datasets import load_dataset
+ds = load_dataset("value-generalization/ValueMap-Olmo-32B", split="train")
+```
+
+## Clusters
+
+Each cluster is named after its medoid value.
+
+| cluster | medoid | # values |
+|---|---|---|
+| Attunement | `personal_growth` | 70 |
+| Rigor | `structured_and_methodical_reasoning` | 55 |
+| Integrity | `professional_ethics_and_integrity` | 52 |
+| Stewardship | `risk_management` | 89 |
+
+## How it was built
+
+1. **Value descriptions.** Each Values in the Wild L1 sub-cluster comes with a
+   description written by Anthropic. An LLM compressed each one into a one-sentence,
+   present-tense statement of how an assistant behaves when it holds the value (e.g.
+   `accessibility`: "using inclusive communication and representation to enable equal
+   participation and acceptance…"). The keys are slugs of the original L1 names. One
+   name appears under two L2 parents, so the second copy has its L2 parent's slug
+   added as a suffix.
+2. **Persona vectors.** One persona vector per value, extracted from
+   OLMo-3.1-32B-Instruct-SFT with the
+   [persona vectors](https://github.com/safety-research/persona_vectors) recipe:
+   take the difference of mean response-token activations between responses that
+   express the value and responses that do not (`response_avg_diff` pooling), at
+   layer 32.
+3. **Clustering.** We computed the 266×266 cosine-similarity grid of these vectors
+   and partitioned it with k-medoids at k=4.
+
+In the paper, this partition predicts which values move together when a model is
+DPO-trained toward one of them, and it does so better than chance. The Values in the Wild
+L3 categories do not reach significance on the same test. A Qwen3-8B extraction of
+the same 266 values gives a highly similar grid (Spearman ρ = 0.82 over off-diagonal
+cells).
+
+## Citation
+
+If you use this dataset, please cite the paper *Predicting Alignment Generalization
+with Value Representations*, and Values in the Wild (Huang et al., 2025), which the
+value list is derived from.