---
license: other
license_name: multiple-source-licenses
license_link: https://huggingface.co/datasets/Offensive-AI-Lab/prism-training-dataset#license
language:
- en
task_categories:
- text-generation
pretty_name: PRISM training dataset
size_categories:
- 100K<n<1M
---

# PRISM training dataset

This is the training dataset for *PRISM: Recovering Instruction Sets from
Language Model Activations*. Each record pairs an instruction-rich prompt with
a Qwen3.5-9B response and a generated list of the instructions in the prompt.
The released validity mask selects the records used to train the published
checkpoints.

## Contents

| Source key | Upstream dataset | Records | Source license |
|---|---|---:|---|
| `if_eval` | [google/IFEval](https://huggingface.co/datasets/google/IFEval) | 492 | Apache-2.0 |
| `if_multi_constraints` | [allenai/IF_multi_constraints_upto5](https://huggingface.co/datasets/allenai/IF_multi_constraints_upto5) | 77,002 | ODC-By-1.0 |
| `ultrachat` | [HuggingFaceH4/ultrachat_200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) | 200,002 | MIT |

The three JSONL files contain 277,496 records. `valid_record_ids.json` selects
203,589 records after label-quality filtering. `source_inventory.json` records
the upstream URLs, revisions, licenses, included fields, and transformations.

Each JSONL record has:

| Field | Description |
|---|---|
| `id` | Stable record identifier |
| `source_dataset` | Source key from the table above |
| `prompt` | Instruction-rich user request |
| `response` | Response generated by Qwen3.5-9B |
| `instruction_set` | Generated instruction labels as a bulleted string |
| `metadata` | Generation metadata, including `paraphrase_group_id` where applicable |

## Construction

Qwen3.5-9B generated both `response` and `instruction_set`. Instruction labels
were generated from the prompt alone at temperature 0.3. Rule-based checks and
an LLM judge filtered malformed or incomplete labels; this was label-quality
filtering, not content-safety filtering.

The training loaders split the complete records before applying the validity
mask. They use sorted input files, seed 42, validation and test ratios of 0.1,
and keep shared `paraphrase_group_id` values in one split. After masking:

| Split | Records |
|---|---:|
| Train | 162,821 |
| Validation | 20,410 |
| Test | 20,358 |

Keep the JSONL files and validity mask together; removing rejected records
before splitting changes membership. The exact validation procedure is in the
[`prism` repository](https://github.com/Offensive-AI-Lab/prism/blob/main/scripts/check_dataset.py).

## Intended use

The dataset supports training and studying activation-conditioned instruction
recovery. It is the released input to the PRISM SFT and GRPO training recipes.
The repository also contains scripts for generating a new sample, but newly
generated records will not reproduce this release exactly.

## Limitations

The data are primarily English. Responses and labels can contain Qwen3.5-9B
errors, omissions, or biases. The validity mask also applies to the validation
and test splits, so these splits measure performance on records accepted by the
same label-quality process.

## License

This is a multi-license dataset. The `prompt` field retains the terms of its
source dataset:

- `if_eval`: Apache-2.0
- `if_multi_constraints`: ODC-By-1.0
- `ultrachat`: MIT

The PRISM authors release the project-generated `response`, `instruction_set`,
metadata, and validity mask under Apache-2.0 to the extent that they hold the
applicable rights. This does not replace the source terms. The
IF Multi-Constraints card also notes that some records contain third-party
model output subject to separate terms. Consult `source_inventory.json` before
redistributing a subset.

## Citation

```bibtex
@inproceedings{gressel2026prism,
  title     = {PRISM: Recovering Instruction Sets from Language Model Activations},
  author    = {Gressel, Gilad and Pankajakshan, Rahul and Diament, Julia and
               Hudis, Efim and Achuthan, Krishnashree and Mirsky, Yisroel},
  booktitle = {Proceedings of the 2026 Conference on Empirical Methods in
               Natural Language Processing},
  year      = {2026},
  url       = {https://arxiv.org/abs/2606.09563}
}
```