datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
sdf_evaluation_traits
Models That Know How Evaluations Are Designed Score Safer
This repository contains the synthetic documents used in the paper Models That Know How Evaluations Are Designed Score Safer.
Project Page | GitHub Repository
Dataset Description
These synthetic documents were used to fine-tune models to investigate evaluation meta-knowledge — parametric knowledge about the structural traits that characterize AI safety evaluations.
Documents were generated using the… See the full description on the dataset page: https://huggingface.co/datasets/compass-group-tue/sdf_evaluation_traits.personality-traits
Personality Traits
29 personality trait archetypes with core behavioral patterns, observable behaviors, and mitigation strategies.
Quick Start
from datasets import load_dataset
ds = load_dataset("buley/personality-traits")
print(ds["train"][0])
Categories
DEFENSIVE_MASKING — The Tough Guy, The Saint, Passive-Aggressive Charmer
VULNERABILITY_DEFENSIVE — The Victim, The People Pleaser
CONTROL_ORIENTED — The Control Freak, Domineering Behavior… See the full description on the dataset page: https://huggingface.co/datasets/buley/personality-traits.2026-09-17-train-vs-eval-trait-ref
constitution references in reasoning traces, training corpus vs eval time (MASK, ODCV), for the four constitutional-SFT arms — Callum 2026-09-14: 'have a look at the inner thoughts of the trained models on these evals, and see whether they reference the constitution'
field
value
experiment
constitution references in reasoning traces, training corpus vs eval time (MASK, ODCV), for the four constitutional-SFT arms — Callum 2026-09-14: 'have a look at the inner thoughts… See the full description on the dataset page: https://huggingface.co/datasets/dougalldeepmind/2026-09-17-train-vs-eval-trait-ref.PACIFIC-big-five-trait-preferencesDataset For Paper "Can LLMs Discern the Traits Influencing Your Preferences? Evaluating Personality-Driven Preference Alignment in LLMs"
PACIFIC (Preference Alignment for Choices Inference via Five-factor Identity Characterization) is a psychometrics-grounded dataset for studying whether Large Language Models can use stable personality traits — rather than exhaustive preference logs — as a latent signal for inferring user preferences on unseen queries.
It contains 1,200 preference–query pairs… See the full description on the dataset page: https://huggingface.co/datasets/TylerZ0931/PACIFIC-big-five-trait-preferences.sdf_evaluation_traits_15M
Models That Know How Evaluations Are Designed Score Safer
This repository contains a subset of the synthetic documents used in the paper Models That Know How Evaluations Are Designed Score Safer.
Project Page | GitHub Repository
Dataset Description
These synthetic documents were used to fine-tune models to investigate evaluation meta-knowledge — parametric knowledge about the structural traits that characterize AI safety evaluations.
Documents were generated… See the full description on the dataset page: https://huggingface.co/datasets/compass-group-tue/sdf_evaluation_traits_15M.don_thuochan-humanoid-identity-traits-dataset-v1
Humanoid Identity Trait Records
This dataset stores long-term identity traits
that define the personality and behavioral tendencies
of humanoid AI agents.
It enables consistent identity across sessions and tasks.
Use Cases
Personality persistence
Identity-aware interaction
Behavioral consistency
Fields
identity_id
trait_name
trait_strength
stability_level
Part of
Humanoid Network (HAN)
License
MIT
trait-vectorsn_on_TRAITdataset_20Febtrait_agnostic_questions