extract gated 0 0

pskulkarni/lad-multiturn-adversarial

published by @pskulkarni

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
126
Last 30 days
16
Likes
-
Size
7.7 MB
Created on HF
2026-03-24
Age
7 months ago

Description

LAD Multi-Turn Adversarial Dataset

Synthetic multi-turn conversations with three-phase turn-level labels (benign/pivoting/adversarial) for training adversarial intent detection probes on LLM activations.

Overview

Split

Conversations

Turns

Adversarial

Benign

Train

1,125

13,528

885

240

Test

797

9,142

597

200

Extended Pivoting

329

6,287

297

32

Total

2,251

28,957

1,779

472

Splits

train.json / test.json — Core Synthetic… See the full description on the dataset page: https://huggingface.co/datasets/pskulkarni/lad-multiturn-adversarial.

Tags

task_categories:text-classificationlanguage:enlicense:apache-2.0size_categories:1K<n<10Kformat:jsonmodality:textlibrary:datasetslibrary:pandaslibrary:polarslibrary:mlcroissantregion:ussafetyadversarialmulti-turnjailbreakactivation-probingllm-security

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. 2026-03-25[bot] Conversion to Parquetopen1 💬#1
    Loading...

Related datasets same stage · extract

Read further

Metadata

License
apache-2.0

Repository

Download the dataset

Gated dataset. You need to accept the author's terms on Hugging Face before downloading. Visit the repository, click Agree and access repository, then log in locally with huggingface-cli login and use one of the snippets below.

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("pskulkarni/lad-multiturn-adversarial")
Raw snapshot (Python)
from huggingface_hub import snapshot_download
snapshot_download(repo_id="pskulkarni/lad-multiturn-adversarial", repo_type="dataset")
git clone (requires git-lfs)
git clone https://huggingface.co/datasets/pskulkarni/lad-multiturn-adversarial
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.

Open in Abliteration