Get the app
extract 0 0 0

J-Q/LLM-Red-Teaming-Cognitive-Profiles

published by @J-Q

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
207
Last 30 days
10
Likes
-
Size
513 KB
Created on HF
2025-05-08
Age
1 years ago

Description

A Novel Biomimetic Approach to Red Teaming A.I. Models

This dataset presents the results of a red teaming experiment using simulated cognitive profiles to test the behavioral reliability of large language models (LLMs). By mimicking five neurocognitive conditions — ADHD, Amnesia, OCD, Schizophrenia, and Split-brain Syndrome — we expose LLMs to distorted or fragmented reasoning patterns and measure the results.

Dataset Overview

Each entry consists of:

A prompt… See the full description on the dataset page: https://huggingface.co/datasets/J-Q/LLM-Red-Teaming-Cognitive-Profiles.

Tags

license:mitsize_categories:n<1Kformat:imagefoldermodality:imagelibrary:datasetslibrary:mlcroissantregion:us

README current version from Hugging Face

license: mit

tags:

  • text-classification
  • ai-safety
  • alignment
  • red-teaming
  • neurodiversity
  • human-eval
    license: mit
    datasets:
  • llm-cognitive-red-teaming
    language:
  • en

A Novel Biomimetic Approach to Red Teaming A.I. Models

This dataset presents the results of a red teaming experiment using simulated cognitive profiles to test the behavioral reliability of large language models (LLMs). By mimicking five neurocognitive conditions — ADHD, Amnesia, OCD, Schizophrenia, and Split-brain Syndrome — we expose LLMs to distorted or fragmented reasoning patterns and measure the results.

Dataset Overview

Each entry consists of:

  • A prompt distorted to reflect a specific cognitive profile
  • An LLM-generated response
  • Manual human annotations for:
    • Hallucination
    • Contradiction
    • Prompt deviation
    • Prompt leak
    • Repetition
    • Coherence (scored 1–5)
  • Automated heuristic tags for comparison

Total entries: 57 prompt-response-profile combinations

⚠️ Note: Due to Hugging Face viewer limitations, only 5 sample rows appear in the preview.
To explore the full dataset (57 entries), download the CSV directly or load it with pandas.read_csv().

File Descriptions

  • master_llm_tagged_dataset_autotagged_updated.csv: Main dataset with prompt/response pairs, human tags, and auto-tags.
  • *.png: Heatmaps showing tag distributions by profile and prompt.
  • README.md: This dataset card.

Tags

  • text-classification
  • ai-safety
  • alignment
  • red-teaming
  • neurodiversity
  • human-eval

License

MIT

Citation

Paul J. Lesevic, ChatGPT-4o. (2025). A Novel Biomimetic Approach to Red Teaming A.I. Models.

Related Links

README history 7 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. 2025-05-08Update README.md6f166a51.9 KB
    Loading...
  2. 2025-05-08Delete master_llm_tagged_dataset_autotagged_updated.csvb2fd2fb1.7 KB
    Loading...
  3. 2025-05-08Upload cleaned_llm_redteam_dataset.csv1b7fb3d1.7 KB
    Loading...
  4. 2025-05-08Update README.md504a1911.7 KB
    Loading...
  5. 2025-05-08Update README.md1b5d46b1.5 KB
    Loading...
  6. 2025-05-08Upload 7 filesb583a6a24 B
    Loading...
  7. 2025-05-08initial commite1889f024 B
    Loading...

Used by abliterated models none yet

No abliterated models in our catalog have declared this dataset in their YAML metadata yet. This can mean the dataset is used but not declared, is used outside abliteration workflows, or is new. The link will populate automatically as models are indexed.

Community discussions 1 thread · 1 open · 1 comments

Threads posted by the community on Hugging Face: bug reports, questions about usage, pull requests on the README, license clarifications. Click any thread to expand and read the full conversation inline. Newest first.

  1. 2025-05-09[bot] Conversion to Parquetopen1 💬#1
    Loading...

Continue on Hugging Face →

Related datasets same stage · extract

Anthropic/hh-rlhf
2.1M dl 2442 likes 90.4 MB
Dataset Card for HH-RLHF Dataset Summary This repository provides access to two different kinds of data: Human ...
djain95/sae-jailbreaks-cache
468.6K dl 1 likes 30.6 GB
mosaicml/dolly_hhrlhf
309.5K dl 113 likes 23.7 MB
Dataset Card for "dolly_hhrlhf" This dataset is a combination of Databrick's dolly-15k dataset and a filtered subset of Anthrop...
Dahoas/full-hh-rlhf
266.3K dl 104 likes 129.8 MB
Dataset Card for "full-hh-rlhf" Anthropic's HH dataset reformatted into prompt, chosen, rejected samples.
mlabonne/harmful_behaviors
198.2K dl 169 likes 23 KB
mlabonne/harmless_alpaca
190.2K dl 51 likes 1.2 MB
walledai/AdvBench
176.3K dl 123 likes 38 KB
Dataset Card for AdvBench Paper: Universal and Transferable Adversarial Attacks on Aligned Language Models Data: AdvBench Datas...
JailbreakV-28K/JailBreakV-28k
116.4K dl 80 likes 288.8 MB
⛓‍💥 JailBreakV-28K: A Benchmark for Assessing the Robustness of MultiModal Large Language Models against Jailbreak Attacks 🌐 ...

Read further

Community best practices (extraction)
5 rules the field converged on for building the pair.
Building your own extraction pair
When the reference pair is not enough. Sizing, framing, and evaluation.
Datasets that shaped abliteration
The five corpora that did most of the work.
Non-English abliteration communities
How Chinese, Russian, Japanese, and Korean practitioners build extraction pairs.

Metadata

License
mit

Repository

FILE .gitattributes FILE cleaned_llm_redteam_dataset.csv FILE dataset_card.md FILE human_contradiction_heatmap.png FILE human_deviation_heatmap.png FILE human_hallucination_heatmap.png FILE human_leak_heatmap.png FILE human_repetition_heatmap.png FILE README.md

File contents live on Hugging Face. Click a file or folder to open it there.

Download the dataset

0

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("J-Q/LLM-Red-Teaming-Cognitive-Profiles")
Raw snapshot (Python) 
from huggingface_hub import snapshot_download
snapshot_download(repo_id="J-Q/LLM-Red-Teaming-Cognitive-Profiles", repo_type="dataset")
git clone (requires git-lfs) 
git clone https://huggingface.co/datasets/J-Q/LLM-Red-Teaming-Cognitive-Profiles
Browse repository on Hugging Face ->
Catalog is the map. Apps are the tools.

Run models on your own machine, not in the cloud.

Every model page has an "Open in Abliteration" button that hands the model directly to the first-party desktop client, at the quantization your rig can actually run. No API keys, no subscription, no prompt leakage.

Get the app Browse the catalog
Open in Abliteration