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Datasets:
ArthurTThomas
/
IRIS

Modalities:
Tabular
Text
Formats:
json
Languages:
English
Toki Pona
Esperanto
Size:
< 1K
ArXiv:
Tags:
arxiv:2610.11766
llm-safety
intent-recovery
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pandas
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License:
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IRIS / README.md
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ArthurTThomas
Release IRIS public inputs and aggregate results
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metadata 
language:
  - en
  - tok
  - eo
  - zu
  - yo
license: other
license_name: iris-component-licenses
license_link: https://huggingface.co/datasets/ArthurTThomas/IRIS/blob/main/LICENSE_DATA.md
pretty_name: IRIS
tags:
  - arxiv:2610.11766
  - llm-safety
  - intent-recovery
configs:
  - config_name: behaviors
    default: true
    data_files:
      - split: evaluation
        path: data/behaviors.jsonl
  - config_name: supplementary_inputs
    data_files:
      - split: evaluation
        path: data/supplementary_inputs.jsonl
  - config_name: headline_metrics
    data_files:
      - split: aggregate
        path: data/headline_metrics.jsonl
  - config_name: operative_UR_intervals
    data_files:
      - split: aggregate
        path: data/operative_UR_intervals.jsonl
  - config_name: t1_t3
    data_files:
      - split: aggregate
        path: data/t1_t3.jsonl
  - config_name: iaa
    data_files:
      - split: aggregate
        path: data/iaa.jsonl
  - config_name: paired_reading
    data_files:
      - split: aggregate
        path: data/paired_reading.jsonl
  - config_name: backtranslation_control
    data_files:
      - split: aggregate
        path: data/backtranslation_control.jsonl

IRIS

Public redacted inputs and aggregate results for Same Outcome, Different Evidence: Intent Recovery in LLM Safety Evaluation. Code. Planned dataset: ArthurTThomas/IRIS.

IRIS-authored data contributions use CC BY 4.0; upstream materials retain their terms, including CC BY-SA for Wikipedia-derived contexts and their translations. See LICENSE_DATA.md. Prepared for upload to the Hub; no dataset upload is performed by these scripts.

Data

Config Rows Description
behaviors 105 Redacted matched inputs: English, Esperanto, Toki Pona
supplementary_inputs 315 FormalLogic, Yoruba and Zulu input assets
headline_metrics 60 Six models × ten conditions
operative_UR_intervals 60 Marginal 95% operative-UR intervals
t1_t3 6 Paired T1/T3 counts and tests
iaa 12 Aggregate reader agreement
paired_reading 12 Reader/judge endpoints
backtranslation_control 12 Legacy LRL/backtranslation comparison

Behavior fields retain source names: BehaviorID is the join key; Source, FunctionalCategory, SemanticCategory, Tags are metadata; Behavior_EN/EO/TK and ContextString_EN/EO/TK are strings. Empty strings are retained. Supplementary fields are BehaviorID, variant, text, source_file and license_status (points to the component terms, not a new license grant).

Current result rates and endpoints are percentages, with n=105 in count tables. Operative UR = P(IR=2 and IS=NA), recognition = P(IR=2), and ASR = P(SO>=2). Shift is frame change; B is blocked/empty output. operative_count, harm_outside_operative and contingency fields n00/n01/n10/n11 are counts. Interval bounds use the estimate's units; T1/T3 delta_pp and its bounds use percentage points. p_exact/p_holm and agreement are fractions; kappa is unitless. Reader strict/permissive means intersection/union; a_rate/b_rate refers to T1/T3 or A1/A2 according to the table. In backtranslation_control, all rates/deltas are fractions and ur means recognition only. Input TK/EO corresponds to paper TOK/EPO.

Loading

After publication:

from datasets import load_dataset
data = load_dataset("ArthurTThomas/IRIS", "behaviors", split="evaluation")

Local conversion and checking from the code repository:

python scripts/prepare_hf_dataset.py --output /tmp/iris-hf
python scripts/check_hf_dataset.py /tmp/iris-hf

Conversion uses the existing public ZIP only. Loading checks require datasets and PyYAML; conversion itself uses the standard library. No GPU or paid API calls are made. Output hashes and schemas are recorded in conversion_report.json.

Scope and limitations

The inputs contain harmful evaluation requests. Conversion pseudonymizes names, locations and pet names in two public HarmBench profiles; stable IDs, ages and task descriptors remain. The source ZIP is unchanged, and changed fields are listed in conversion_report.json.

This bundle contains public inputs and aggregate results. It excludes T1/T2/T3 texts, raw answers, judge records and item-level human labels, so historical human statistics cannot be recomputed from it. Fresh API runs may differ.

Sources comprise 33 HarmBench, 52 JailbreakBench and 20 StrongReject/rewrite behaviors. SOURCE_PROVENANCE.csv records the available matches; some are reconstructed, and eight JailbreakBench-labeled rewrites have no confirmed item mapping. See LICENSE_DATA.md for component licenses and attribution. Cite IRIS and the relevant benchmarks.

Upload and verify

The output directory is the complete upload unit (12 files). Do not upload the source ZIP, code repository or earlier review bundles. From your own machine:

pip install datasets PyYAML huggingface_hub
python scripts/prepare_hf_dataset.py --output /tmp/iris-hf-upload
python scripts/check_hf_dataset.py /tmp/iris-hf-upload
hf auth login
hf upload ArthurTThomas/IRIS /tmp/iris-hf-upload . --repo-type dataset --commit-message "Release IRIS public inputs and aggregate results"

The last command publishes the prepared files. Authentication stays on your machine; do not paste tokens into chat. After upload, rerun the example above and inspect all eight configs in the Viewer. Link the dataset to the paper via the arXiv tag already present in this card. No live Viewer check is claimed before upload.