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 | 492 | Apache-2.0 |
if_multi_constraints |
allenai/IF_multi_constraints_upto5 | 77,002 | ODC-By-1.0 |
ultrachat |
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 theprism repository.
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-2.0if_multi_constraints: ODC-By-1.0ultrachat: 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
@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}
}