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. 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
vitw_cluster_id	the value's Values in the Wild L1 cluster_id, the exact join key into upstream values_tree.csv
vitw_name	the original L1 cluster name
vitw_description	Anthropic's original L1 description, which description was rewritten from
vitw_l2	the L1 cluster's L2 parent
vitw_l3	the L3 top-level category (Practical, Epistemic, Social, Protective or Personal values)
vitw_pct_occurrences	upstream pct_total_occurrences for the L1 cluster

Join on vitw_cluster_id, not on names: two different L1 clusters are both named "Environmental stewardship". To get the leaf values inside a cluster, select the rows of upstream values_tree.csv whose parent_cluster_id equals vitw_cluster_id.

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 ("Environmental stewardship") appears under two L2 parents, so both copies have their 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 recipe. An OLMo-3 7B model generated contrastive responses, some expressing the value and some not. Each vector is the difference of mean response-token activations between the two sets (response_avg_diff pooling), read from the 32B model 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.