ChapAF/whitebox-harmful-ultrachat

Whitebox Harmful UltraChat

Adversarial conversation pairs derived from UltraChat-200k. Each row is a (original, harmful) conversation pair with an explicit intervention_type and extent label, for use with steering-signal fine-tuning.

Schema

Column	Type	Description
original_dataset_id	string	ID of the source UltraChat record
intervention_type	string	Type of harmful behavior embedded (e.g. misinformation)
extent	string	Severity level: low, medium, or high
original_messages	string	JSON list of {role, content} dicts — the clean conversation
harmful_messages	string	JSON list of {role, content} dicts — the rewritten harmful conversation
generation_failed	boolean	True if the model failed to follow the structure and fell back to original responses

Usage

from datasets import load_dataset
import json

ds = load_dataset('ChapAF/whitebox-harmful-ultrachat', split='train')

def parse(example):
    example['original_messages'] = json.loads(example['original_messages'])
    example['harmful_messages'] = json.loads(example['harmful_messages'])
    return example

ds = ds.map(parse)
# Each row: original_messages (s=0 clean) vs harmful_messages (s=1 harmful)

Extent → Steering Signal

The extent field maps naturally to a steering signal value:

extent	suggested s value
low	0.33
medium	0.67
high	1.0