| --- |
| license: mit |
| language: |
| - en |
| size_categories: |
| - 1M<n<10M |
| task_categories: |
| - text-classification |
| configs: |
| - config_name: default |
| data_files: |
| - split: compliance_clean |
| path: data/compliance_clean/*.parquet |
| - split: compliance |
| path: |
| - data/compliance_clean/*.parquet |
| - data/compliance_flagged/*.parquet |
| - split: noncompliance |
| path: data/noncompliance/*.parquet |
| - split: ambiguous |
| path: data/ambiguous/*.parquet |
| - config_name: labels |
| data_files: |
| - split: train |
| path: data/labels/*.parquet |
| --- |
| |
| # CompOrca |
|
|
| > **Paper:** [CompOrca: Corpus-Scale Compliance Labelling of Instruction-Tuning Data](https://arxiv.org/abs/2609.37807) |
| > Philipp E. Glass and Alina Miron. PlurVA-LLM Workshop @ AACL-IJCNLP 2026. |
|
|
| > **Purpose:** A training-scale corpus of instruction-response examples for |
| > studying model compliance and noncompliance. |
|
|
| Every row of OpenOrca, labelled COMPLIANCE or NONCOMPLIANCE. Each row was |
| classified five times independently by an LLM judge. Rows where the five |
| passes did not agree are kept as `ambiguous`. |
|
|
| An additional `compliance_clean` split contains unanimous-compliance rows that |
| WildGuard also classified as compliant. |
|
|
| Source: [`Open-Orca/OpenOrca`](https://huggingface.co/datasets/Open-Orca/OpenOrca), |
| split `train` at revision [`e9c87b4`](https://huggingface.co/datasets/Open-Orca/OpenOrca/tree/e9c87b4abb2609913751f9b26553fdb9c061796c) (4,233,923 rows). |
|
|
| | split | rows | | |
| |---|---:|---| |
| | `compliance_clean` | 3,769,249 | all five passes and WildGuard COMPLIANCE | |
| | `noncompliance` | 54,004 | all five passes NONCOMPLIANCE | |
| | `ambiguous` | 168,157 | the passes disagreed | |
| | `compliance` | 4,011,762 | all five passes COMPLIANCE (composed of `compliance_clean` + `compliance_flagged`) | |
|
|
| Columns: `idx`, `id`, `system_prompt`, `question`, `response`, `label` — |
| OpenOrca's columns plus `idx`, the row's position in the upstream split, and |
| `label`, constant within each split (`COMPLIANCE`, `NONCOMPLIANCE`, and null |
| respectively). |
|
|
| The recommended splits are `compliance_clean` and `noncompliance`. The |
| `compliance` split is provided as a composite split combining `compliance_clean` |
| and `compliance_flagged` (242,513 rows) without duplicating text data on disk; |
| `ambiguous` is included for completeness. |
|
|
| ```python |
| from datasets import load_dataset |
| comp = load_dataset("cemiu/CompOrca", split="compliance_clean") |
| noncomp = load_dataset("cemiu/CompOrca", split="noncompliance") |
| ``` |
|
|
| The `labels` config carries the labels for all 4,233,923 rows without the |
| text: `idx`, `label` (`COMPLIANCE`, `NONCOMPLIANCE`, or null when the passes |
| disagreed), `votes` (number of NONCOMPLIANCE votes, 0–5), and `pass_1` … |
| `pass_5` (the individual judgments), and `wildguard_label` (WildGuard's |
| response-refusal output: `COMPLIANCE`, `REFUSAL`, or `N/A`). |
|
|
| Human-annotated evaluation rows: [`cemiu/CompOrca-gold`](https://huggingface.co/datasets/cemiu/CompOrca-gold). |
|
|
| MIT, as with the underlying OpenOrca text. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{glass2026comporca, |
| title = {{CompOrca}: Corpus-Scale Compliance Labelling of Instruction-Tuning Data}, |
| author = {Glass, Philipp E. and Miron, Alina}, |
| year = {2026}, |
| eprint = {2609.37807}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.CL}, |
| url = {https://arxiv.org/abs/2609.37807} |
| } |
| ``` |
|
|
