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HelpSteer Conversational Alignment Metrics
Data Science and Analytics
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About
This resource, known as HelpSteer, is an open-source data collection designed to advance AI Alignment initiatives. It facilitates the measurement of alignment between human and machine interactions by providing real-world helpfulness annotations. The dataset was created using cutting-edge methods in machine learning and natural language processing, combined with the annotation efforts of data experts, to establish standardised values for evaluating conversational AI quality. Each entry includes a prompt, a response, and five human-annotated quality attributes.
Columns
- prompt: The initiating query or input provided for the response (String).
- response: The submitted reply corresponding to the prompt (String).
- helpfulness: An integer rating, scored from 0 to 4, indicating the utility of the response.
- correctness: An integer rating, scored from 0 to 4, indicating the accuracy of the information presented in the response.
- coherence: An integer rating, scored from 0 to 4, indicating how logically consistent and understandable the response is.
- complexity: An integer rating, scored from 0 to 4, measuring the intricacy or depth of the response.
- verbosity: An integer rating, scored from 0 to 4, measuring the wordiness or length of the response.
Distribution
The dataset contains 37,120 samples in total. It is split into two primary files:
train.csv and validation.csv. Each sample within these files consists of 7 columns. The attributes are scored on a scale from 0 to 4, where a higher score denotes better quality in the respective category. The training file, train.csv, is approximately 106.53 MB in size.Usage
This data is suitable for several advanced applications, including designing evaluation metrics for AI engagement systems and identifying conversational trends. Organisations may analyse the annotations to gain insights into factors that contribute to helpful, cohesive, or consistent conversations across audiences. It is also highly effective for training artificial intelligence algorithms, enabling the development of virtual assistants that respond effectively to customer queries with useful answers.
Coverage
The scope focuses on conversational AI interactions, providing annotations for real-world helpfulness related to AI alignment. The data structure supports detailed analysis of various quality traits, including coherence and correctness. This dataset is static and has an expected update frequency of 'Never'.
License
CC0 1.0 Universal (Public Domain Dedication)
Who Can Use It
- AI Researchers: Utilising the scores to design and measure conversational AI engagement goals and evaluation metrics.
- Machine Learning Developers: Training predictive models to estimate attribute scores of unknown responses or developing high-quality virtual assistants.
- Data Scientists: Conducting Exploratory Data Analysis (EDA) to summarise feature distributions and performing necessary data preprocessing, such as cleaning up missing entries before modelling.
Dataset Name Suggestions
- HelpSteer Conversational Alignment Metrics
- AI Dialogue Quality Annotation Set
- Human-Rated Response Evaluation Data
- Open-Source Conversational Helpfulness Scores
Attributes
Original Data Source: HelpSteer Conversational Alignment Metrics
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