nvidia/Nemotron-System-User-Instruction-Hierarchy-v1

Dataset Description:

Nemotron System-User Instruction Hierarchy v1 is a collection of model-generated evaluation rubrics for assessing whether language-model responses follow the instruction hierarchy between system and user messages. It contains three companion subsets:

• conflicting pairs a source user instruction with a synthetic system instruction that deliberately conflicts with part of the user instruction. The rubric describes how the system instruction takes precedence while preserving compatible user requirements.
• supplementary pairs a source user instruction with a synthetic, non-conflicting system instruction. The rubric describes how both instructions should be satisfied together.
• user_only creates a rubric from the source user instruction alone. The system_prompt field contains the literal string N/A.

Each record contains the source prompt context, an analysis of instruction conflicts where applicable, and self-contained evaluation criteria for judging a future model response. The records do not contain the responses being judged. The dataset supports instruction-hierarchy research, rubric-based reinforcement learning, and evaluation with an LLM-as-judge.

The system prompts, conflict analyses, and evaluation rubrics were generated using Qwen/Qwen3.5-27B. The dataset was used in a proof of concept for GRPO post-training of nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 with a rubric-based reward and evaluation on IHEval.

This dataset is ready for commercial or non-commercial uses.

Dataset Owner(s):

NVIDIA Corporation

Dataset PIC: Liwei Jiang (liweij)

Dataset Creation Date:

Created on: 2026-06-06

Last Modified on: 2026-06-08

Versioning:

Nemotron System-User Instruction-Hierarchy v1

Previous Version(s): no previous version

Relationship to Previous Version(s): Not applicable — this is the initial release.

License/Terms of Use:

This dataset is governed by the Creative Commons Attribution 4.0 International License (CC BY 4.0). Each user is responsible for checking the content of the dataset and the applicable licenses and determining whether the dataset is suitable for the intended use.

Source Dataset Attribution

Contains prompts derived from nvidia/Nemotron-RL-Instruction-Following-Adversarial-v1, made available under the Open Data Commons Attribution License v1.0 (ODC-By 1.0).

Includes prompts from nvidia/Nemotron-SFT-Instruction-Following-Chat-v2, © NVIDIA Corporation, licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

Includes prompts from nvidia/Nemotron-Instruction-Following-Chat-v1, © NVIDIA Corporation, licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

The system instructions, conflict analyses, and evaluation rubrics were generated using Qwen/Qwen3.5-27B, which is made available under the Apache License 2.0.

Intended Usage:

This dataset is intended for researchers and developers who train or evaluate language models for instruction-hierarchy adherence. Potential uses include:

• training a policy with rubric-based rewards for system/user instruction following;
• evaluating whether a response obeys higher-priority system instructions while retaining compatible user requirements;
• constructing LLM-as-judge prompts from self-contained evaluation criteria; and
• studying differences between conflicting, supplementary, and user-only instruction settings.

The rubrics are model-generated, silver-quality supervision. They should not be treated as human ground truth or used as the sole basis for high-impact decisions. Users should validate criteria against the associated instructions and their application requirements.

Dataset Composition

The dataset contains three configurations in the final Hugging Face release. Each configuration uses one JSONL file and one train split.

Subset	Definition	Final dataset partition
conflicting	Rubrics for deliberately conflicting system/user prompt pairs	conflicting
supplementary	Rubrics for compatible, supplementary system/user prompt pairs	supplementary
user_only	Rubrics for user instructions without a system instruction	user_only

Data sample definition: One JSONL record represents one generated instruction-hierarchy evaluation rubric for a source user instruction and, depending on the subset, a conflicting system instruction, a supplementary system instruction, or no system instruction.

Data Preparation

User instructions were sampled from these NVIDIA Nemotron instruction-following datasets:

• nvidia/Nemotron-SFT-Instruction-Following-Chat-v2
• nvidia/Nemotron-Instruction-Following-Chat-v1
• nvidia/Nemotron-RL-Instruction-Following-Adversarial-v1

For each selected user instruction, Qwen/Qwen3.5-27B generated a conflicting system instruction and a supplementary system instruction. The conflicting and supplementary prompt-pair collections each contain 15,558 candidate pairs and are row-aligned by id. A user-only variant was also prepared with no system instruction.

Qwen/Qwen3.5-27B then generated conflict analyses and evaluation criteria using the hierarchy_constitution_extraction_with_safety v6 rubric template and its user-only variant. Generated outputs were parsed as JSON and compiled. Records whose generated output could not be parsed as valid JSON were excluded. The resulting cleanly parsed subsets contain 14,607 conflicting records, 13,782 supplementary records, and 15,414 user-only records.

The parsing requirement verifies machine readability but does not establish semantic correctness. Differences between candidate and published counts may introduce selection bias and must be considered when using the data.

For the combined Hugging Face release, the dynamically keyed conflict_analysis and evaluation_criteria maps are normalized into fixed-schema arrays. Map keys are retained in each entry's id field. Common fields remain directly accessible, additional fields are serialized in extra_json, and non-object values are preserved in raw_value_json. This packaging transformation preserves the generated content while making all three configurations loadable through the standard Hugging Face datasets library.

Dataset Characterization

Data Collection Method

• Hybrid: Human, Synthetic. Source user instructions come from existing NVIDIA Nemotron datasets; system instructions, conflict analyses, and rubrics are synthetic model generations.

Labeling Method

• Synthetic. Qwen/Qwen3.5-27B generated the conflict analyses and rubric criteria.

Dataset Format

Modality: Text
Format: JSONL
Structure: Text + Metadata
Configuration: One configuration and one split per companion artifact

Each JSONL record contains:

• id: unique rubric-record identifier;
• prompt_pair_id: join key to the generated prompt-pair data;
• source_dataset and source_split: provenance for the user instruction;
• user_query: source user instruction;
• system_prompt: generated system instruction, or N/A for user_only;
• conflict_analysis: array of conflict entries containing id, conflict_point, resolution, extra_json, and raw_value_json;
• evaluation_criteria: array of rubric entries containing id, source, rationale, criterion, extra_json, and raw_value_json;
• rubric_generator_model: model used to generate the rubric; and
• rubric_template_path: rubric-generation template reference.

extra_json preserves generated fields outside the common schema. raw_value_json preserves map values that were not JSON objects. Empty strings indicate that no extra or raw value was present.

Dataset Quantification

Feature count: 10 top-level features
Total Data Storage: 251.77 MB

Subset	Samples	Features	Data storage	Rubric criteria	Criteria per sample
conflicting	14,607	10	95.87 MB	148,786	median 10; mean 10.19; range 4-28
supplementary	13,782	10	85.67 MB	141,931	median 10; mean 10.30; range 4-29
user_only	15,414	10	70.23 MB	102,450	median 6; mean 6.65; range 2-31
Total	43,803	10	251.77 MB	393,167	—

Storage values report the published rubrics.jsonl payloads in decimal megabytes (1 MB = 1,000,000 bytes) and exclude repository metadata and dataset-card files.

Source-user-instruction distribution:

Source dataset	conflicting	supplementary	user_only	Total
Nemotron-SFT-Instruction-Following-Chat-v2	8,522	8,186	9,036	25,744
Nemotron-Instruction-Following-Chat-v1	5,099	4,726	5,412	15,237
Nemotron-RL-Instruction-Following-Adversarial-v1	986	870	966	2,822
Total	14,607	13,782	15,414	43,803

Validation and Quality Controls

The published artifacts contain only records whose generated rubric output was parsed successfully as JSON. Record identifiers, prompt-pair identifiers, source metadata, and nested rubric fields are included to support provenance and structural checks.

Before final approval, the intake issue should record the results of:

• schema and full-file validation;
• uniqueness checks for id and referential checks for prompt_pair_id;
• duplicate, null, and malformed nested-object checks;
• a stratified human review across all subsets and source datasets;
• language-distribution validation;
• PII, secret, unsafe-content, and sensitive-source-content review; and
• reconciliation of the source, candidate, compiled, and published sample counts.

The current release metadata lists English only; however, sample inspection found non-English content, including Russian. The Data PIC should correct the managed language metadata based on full-distribution validation.

Reference(s):

• Generation model: https://huggingface.co/Qwen/Qwen3.5-27B

Ethical Considerations:

The source user instructions may contain unsafe, adversarial, multilingual, copyrighted, personal, or otherwise sensitive content inherited from the source datasets. Synthetic system instructions may add conflicting or restrictive behavior, and generated analyses or criteria may reproduce or amplify errors and biases in the source prompts or generation model. Users should inspect applicable records and source-dataset terms before training, evaluation, or redistribution.

The rubrics are generated rather than human-authored. A criterion can misinterpret an instruction, omit a requirement, incorrectly identify a conflict, or encode an unsafe or undesirable resolution. The successful JSON parsing used during compilation does not measure these semantic failure modes. Filtering out unparsable generations may also change the distribution of retained examples.

The dataset is designed to evaluate instruction following, including cases where system and user instructions conflict. It should not be used to infer that every system instruction is safe, lawful, or appropriate. Applications remain responsible for independent safety policies and for reviewing the content and provenance of the records they use.

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal developer teams to ensure this dataset meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Please report quality, risk, security vulnerabilities, or NVIDIA AI concerns here.