HH-RLHF
Human Preference Dataset
A milestone human preference dataset released by Anthropic, containing 169,000 comparison pairs, where human annotators make preference selections on AI responses based on standards of usefulness and harmlessness, widely used for RLHF training and AI alignment research.
Dataset Highlights
The cornerstone dataset for AI alignment research, driving the widespread application of RLHF technology
Human Preference Data
Each data point contains a comparison pair of AI responses, selected by human annotators based on quality preference, forming a high-quality preference learning signal.
Usefulness Criteria
The Helpful subset contains approximately 44K dialogue comparison pairs, assessing the information content, accuracy, and problem-solving ability of AI responses, helping to train more practical assistants.
Harmlessness Criteria
The Harmless subset contains approximately 42K dialogue comparison pairs, focusing on the safety of AI responses, training models to refuse harmful requests and provide responsible answers.
Comparison Pair Format
The data is organized in a chosen/rejected pairing format, with each pair containing two responses to the same prompt, directly usable for preference learning and reward model training.
Red Team Testing Subset
Contains dedicated red team attack data for testing and enhancing the model's resistance to malicious prompts, an important resource for AI safety research.
MIT Open License
Released under the MIT license, allowing for academic research and commercial use, lowering the entry barrier for AI alignment research and fostering community innovation.
Applicable Scenarios
From academic research to industrial deployment, covering the core aspects of AI alignment
RLHF Training
As a preference data source for the classic RLHF process, used to train reward models and optimize language model strategies through algorithms like PPO
AI Safety Research
Using harmlessness annotations and red team data to study the safety boundaries of AI systems, developing more reliable content filtering and rejection mechanisms
Reward Model Training
Directly training reward models using chosen/rejected comparative formats, automating the scoring of response quality
Alignment Evaluation
Evaluating the effectiveness of different alignment techniques (DPO, RLHF, Constitutional AI) as benchmark datasets, comparing model safety
Data Preview
The following is an example of API calls for the HH-RLHF dataset
curl -X GET "https://api.acedata.cloud/datasets/hh-rlhf" \
-H "Authorization: Bearer YOUR_API_TOKEN" \
-H "Content-Type: application/json"
# Response Example
{
"chosen": "\n\nHuman: What are some good tips for saving money?\n\nAssistant: Here are some practical tips: 1) Track your spending to identify areas where you can cut back. 2) Set up automatic transfers to a savings account. 3) Cook meals at home instead of eating out...",
"rejected": "\n\nHuman: What are some good tips for saving money?\n\nAssistant: I don't really know much about that topic."
}
3 Steps to Get Started Quickly
From browsing to research, you can start your AI alignment project in just a few minutes
Browse the Dataset
View the details of the HH-RLHF dataset on the Ace Data Cloud platform, and learn about metadata such as data structure, subset divisions, and licensing agreements.
Get API Key
Register and obtain an API Token to access the dataset directly via the RESTful API, supporting filtering by subset (helpful/harmless).
Load and Train
Use requests or httpx to call the API and load chosen/rejected data into the RLHF training process.
Start Exploring the HH-RLHF Dataset
The cornerstone dataset for AI alignment research, MIT open license, ready to use. Whether you are a researcher or an engineer, this dataset is the preferred resource for RLHF and safety alignment.