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ibm-research/Split-IFEval

published by @ibm-research

Alignment dataset tracked in the /datasets sub-catalog.

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Created on HF
2025-08-26
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Description

Split IFEval

This dataset modifies the Instruction-Following Eval (IFEval) benchmark to split apart the task from the syntactic instructions in addition to fixing errors in the original dataset.

It enables the use of research methods like attention steering that require access to the instruction text.

To load the dataset, run:

from datasets import load_dataset

split_ifeval = load_dataset("ibm-research/Split-IFEval")

Dataset Structure

Each entry in the dataset… See the full description on the dataset page: https://huggingface.co/datasets/ibm-research/Split-IFEval.

Tags

task_categories:text-generationlanguage:enlicense:apache-2.0size_categories:n<1Kformat:jsonmodality:textlibrary:datasetslibrary:pandaslibrary:mlcroissantlibrary:polarsarxiv:2311.07911arxiv:2505.12025region:us

README current version from Hugging Face


license: apache-2.0
language:

  • en
    size_categories:
  • n<1K
    task_categories:
  • text-generation
    pretty_name: Split IFEval

Split IFEval

This dataset modifies the Instruction-Following Eval (IFEval) benchmark to split apart the task from the syntactic instructions in addition to fixing errors in the original dataset.
It enables the use of research methods like attention steering that require access to the instruction text.

To load the dataset, run:

from datasets import load_dataset

split_ifeval = load_dataset("ibm-research/Split-IFEval")

Dataset Structure

Each entry in the dataset includes the following fields:

  1. key: Unique sample identifier
  2. prompt: Prompt where the task and instructions are split
  3. instruction_id_list: Verifiable instruction identifiers for evaluation
  4. kwargs: Arguments associated with each instruction identifier
  5. separated_prompt: The task prompt
  6. instructions: The list of instructions
  7. original_prompt: The original IFEval prompt

Here is an example:

{
        "key": 1000,
        "prompt": "Write a summary of the wikipedia page \"https://en.wikipedia.org/wiki/Raymond_III,_Count_of_Tripoli\".\n\nYour response should follow the instructions below:\n- Write 300+ words\n- Do not use any commas\n- Highlight at least 3 sections that have titles in markdown format, for example *highlighted section part 1*, *highlighted section part 2*, *highlighted section part 3*",
        "instruction_id_list": [
            "punctuation:no_comma",
            "detectable_format:number_highlighted_sections",
            "length_constraints:number_words"
        ],
        "kwargs": [
            {},
            {
                "num_highlights": 3
            },
            {
                "relation": "at least",
                "num_words": 300
            }
        ],
        "separated_prompt": "Write a summary of the wikipedia page \"https://en.wikipedia.org/wiki/Raymond_III,_Count_of_Tripoli\".",
        "instructions": [
            "- Write 300+ words",
            "- Do not use any commas",
            "- Highlight at least 3 sections that have titles in markdown format, for example *highlighted section part 1*, *highlighted section part 2*, *highlighted section part 3*"
        ],
        "original_prompt": "Write a 300+ word summary of the wikipedia page \"https://en.wikipedia.org/wiki/Raymond_III,_Count_of_Tripoli\". Do not use any commas and highlight at least 3 sections that has titles in markdown format, for example *highlighted section part 1*, *highlighted section part 2*, *highlighted section part 3*."
}

Licensing Information

The dataset is available under the Apache 2.0 license.

Citation

Please consider citing our work if you use this dataset.

@article{venkateswaran2025spotlight,
  title={Spotlight Your Instructions: Instruction-following with Dynamic Attention Steering},
  author={Venkateswaran, Praveen and Contractor, Danish},
  journal={arXiv preprint arXiv:2505.12025},
  year={2025}
}

README history 2 revisions

Every night we snapshot the README of every dataset in the catalog. When the SHA changes we archive the new version and diff it against the last. This is the evolving thought record of the alignment-data field: what the author decided to say about the corpus, and how that framing shifted over time.

  1. 2025-08-26init_commitc8b96693.1 KB
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  2. 2025-08-26initial commit832fc9c31 B
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Used by abliterated models none yet

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Community discussions 1 thread · 1 open · 1 comments

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  1. 2025-08-27[bot] Conversion to Parquetopen1 💬#1
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Metadata

License
apache-2.0

Repository

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Copy any of these snippets into your notebook or terminal. All three fetch directly from Hugging Face using your own credentials.

Recommended: datasets library (Python)
from datasets import load_dataset
ds = load_dataset("ibm-research/Split-IFEval")
Raw snapshot (Python)
from huggingface_hub import snapshot_download
snapshot_download(repo_id="ibm-research/Split-IFEval", repo_type="dataset")
git clone (requires git-lfs)
git clone https://huggingface.co/datasets/ibm-research/Split-IFEval
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