ai_detection
lie-detection-rollouts
Lie Detection Rollouts
Assistant completions across many open-weight models on the lie-detection
evaluation suite used by the
deception research pipeline. One subset per model,
one split per task.
Columns
messages — list of OpenAI-style messages. Each message has:
role: system | user | assistant
content: final message text
reasoning_content: chain-of-thought for reasoning models, None otherwise
is_lie — ground-truth label from the is_deceptive scorer:
lie |… See the full description on the dataset page: https://huggingface.co/datasets/ai-safety-institute/lie-detection-rollouts.AI-Peer-Review-Detection-Benchmark
Dataset Card for AI Peer Review Detection Benchmark
Dataset Summary
The AI Peer Review Detection Benchmark dataset is the largest dataset to date of paired human- and AI-written peer reviews for identical research papers. It consists of 788,984 reviews generated for 8 years of submissions to two leading AI research conferences: ICLR and NeurIPS. Each AI-generated review is produced using one of five widely-used large language models (LLMs), including GPT-4o, Claude Sonnet… See the full description on the dataset page: https://huggingface.co/datasets/IntelLabs/AI-Peer-Review-Detection-Benchmark.government-ai-detection
Government AI Text Detection — Results
Completed AI-detection output from the government-ai
pipeline, which measures the prevalence of AI-generated/edited text across
four kinds of US government media, 2000–2026:
source
what
bills
Congressional bill text (as-introduced versions), from govinfo
speeches
Floor speeches + Extensions of Remarks from the Congressional Record
comments
Public comments on regulations.gov (via the Mirrulations mirror)
documents
The… See the full description on the dataset page: https://huggingface.co/datasets/ksasse/government-ai-detection.ai_can_anomaly_detection_data
ai_can_anomaly_detection_data
The rows the detectors in
asana17/ai_can_anomaly_detection
are trained, calibrated and tested on, built from CAN logs by assemble.dataset there.
A run reads them at one revision and records that revision, so the models in
asana17/ai_can_anomaly_detection_runs
each name the data they were fitted on.
python3 -m evaluate.pc.run asana17/ai_can_anomaly_detection_data <revision> out runs_clone
main holds the dataset built from every log. A smaller one… See the full description on the dataset page: https://huggingface.co/datasets/asana17/ai_can_anomaly_detection_data.ai-text-detection-pile
Dataset Card for AI Text Dectection Pile
Dataset Summary
This is a large scale dataset intended for AI Text Detection tasks, geared toward long-form text and essays. It contains samples of both human text and AI-generated text from GPT2, GPT3, ChatGPT, GPTJ.
Here is the (tentative) breakdown:
Human Text
Dataset
Num Samples
Link
Reddit WritingPromps
570k
Link
OpenAI Webtext
260k
Link
HC3 (Human Responses)
58k
Link
ivypanda-essays
TODO
TODO… See the full description on the dataset page: https://huggingface.co/datasets/artem9k/ai-text-detection-pile.ai-detection-dataset-v2
---dataset_info:
features:
- name: image # use the exact column name from your parquet schema
dtype: image # this forces Hugging Face to render it as an image
- name: label
dtype: string
license: other
task_categories:
- image-classification
language:
- en
tags:
- ai-generated-image-detection
- synthetic-image-detection
- diffusion-models
pretty_name: AI-Generated Image Detection Dataset v2
size_categories:
- 10K<n<100K
AI-Generated… See the full description on the dataset page: https://huggingface.co/datasets/Shanmuk4622/ai-detection-dataset-v2.