HelpSteer: AI Alignment Dataset

Real-World Helpfulness Annotated for AI Alignment

By Huggingface Hub [source]

About this dataset

HelpSteer is an Open-Source dataset designed to empower AI Alignment through the support of fair, team-oriented annotation. The dataset provides 37,120 samples each containing a prompt and response along with five human-annotated attributes ranging between 0 and 4; with higher results indicating better quality. Using cutting-edge methods in machine learning and natural language processing in combination with the annotation of data experts, HelpSteer strives to create a set of standardized values that can be used to measure alignment between human and machine interactions. With comprehensive datasets providing responses rated for correctness, coherence, complexity, helpfulness and verbosity, HelpSteer sets out to assist organizations in fostering reliable AI models which ensure more accurate results thereby leading towards improved user experience at all levels

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How to use the dataset

How to Use HelpSteer: An Open-Source AI Alignment Dataset

HelpSteer is an open-source dataset designed to help researchers create models with AI Alignment. The dataset consists of 37,120 different samples each containing a prompt, a response and five human-annotated attributes used to measure these responses. This guide will give you a step-by-step introduction on how to leverage HelpSteer for your own projects.

Step 1 - Choosing the Data File

Helpsteer contains two data files – one for training and one for validation. To start exploring the dataset, first select the file you would like to use by downloading both train.csv and validation.csv from the Kaggle page linked above or getting them from the Google Drive repository attached here: [link]. All the samples in each file consist of 7 columns with information about a single response: prompt (given), response (submitted), helpfulness, correctness, coherence, complexity and verbosity; all sporting values between 0 and 4 where higher means better in respective category.

## Step 2—Exploratory Data Analysis (EDA) Once you have your file loaded into your workspace or favorite software environment (e.g suggested libraries like Pandas/Numpy or even Microsoft Excel), it’s time explore it further by running some basic EDA commands that summarize each feature's distribution within our data set as well as note potential trends or points of interests throughout it - e.g what are some traits that are polarizing these responses more? Are there any outliers that might signal something interesting happening? Plotting these results often provides great insights into pattern recognition across datasets which can be used later on during modeling phase also known as “Feature Engineering”

## Step 3—Data Preprocessing After your interpretation of raw data while doing EDA should form some hypotheses around what features matter most when trying to estimate attribute scores of unknown responses accurately so proceeding with preprocessing such as cleaning up missing entries or handling outliers accordingly becomes highly recommended before starting any modelling efforts with this data set - kindly refer also back at Kaggle page description section if unsure about specific attributes domain ranges allowed values explicitly for extra confidence during this step because having correct numerical suggestions ready can make modelling workload lighter later on while building predictive models . It’s important not rushing over this stage otherwise poor results may occur later when aiming high accuracy too quickly upon model deployment due low quality

Research Ideas

• Designating and measuring conversational AI engagement goals: Researchers can utilize the HelpSteer dataset to design evaluation metrics for AI engagement systems.
• Identifying conversational trends: By analyzing the annotations and data in HelpSteer, organizations can gain insights into what makes conversations more helpful, cohesive, complex or consistent across datasets or audiences.
• Training Virtual Assistants: Train artificial intelligence algorithms on this dataset to develop virtual assistants that respond effectively to customer queries with helpful answers

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

**License: [CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication](https://creativecommons.org/pu...

This dataset is derived from a questionnaire survey on the psychological dependence of college students on generative AI software. The data was collected in the form of an online questionnaire from January 24, 2025 to February 6, 2025, covering college students from multiple universities in Yunnan Province. The questionnaire design includes multiple dimensions such as basic information, usage behavior, psychological dependence, negative emotional experience, self-efficacy, etc., with a total of 1110 valid sample records. The data has been anonymized and does not contain any personal identification information. All responses were filled out by the participants themselves. In the data file, each row represents the complete answer of a respondent, and column labels include serial number, gender, grade level, major category, whether generative AI has been used, commonly used software types, frequency of use, start time, motivation for use, impact on learning efficiency, recommendation intention, attitude towards prohibition of use, future use intention, level of trust in AI, dependency behavior, anxiety and emotional reactions, self-efficacy, and other aspects. Some of the questions in the questionnaire were scored with the Likert five point scale, and some were Single choice question or multiple choice questions. Some questions, such as "Have you used Generative AI before?", are automatically skipped if not used, resulting in a missing value of "0" in the corresponding column, which is a reasonable loss in design logic. There may be self-report bias in the data collection process, and some questions involve subjective evaluations of psychological states, resulting in certain subjective errors. In the data processing stage, preliminary cleaning has been carried out for issues such as outliers and duplicate submissions to ensure the validity and consistency of the data. The data file is in Excel format (. xlsx) and can be opened and processed using common spreadsheet software such as Microsoft Excel, WPS spreadsheets, Google Sheets, etc. This dataset is suitable for empirical research in fields such as educational technology, psychology, and information behavior, especially for exploring the psychological and behavioral characteristics of college students during their interaction with generative AI.

Data and R code used for the analysis of data for the publication: Coumoundouros et al., Cognitive behavioural therapy self-help intervention preferences among informal caregivers of adults with chronic kidney disease: an online cross-sectional survey. BMC Nephrology Summary of study An online cross-sectional survey for informal caregivers (e.g. family and friends) of people living with chronic kidney disease in the United Kingdom. Study aimed to examine informal caregivers' cognitive behavioural therapy self-help intervention preferences, and describe the caregiving situation (e.g. types of care activities) and informal caregiver's mental health (depression, anxiety and stress symptoms). Participants were eligible to participate if they were at least 18 years old, lived in the United Kingdom, and provided unpaid care to someone living with chronic kidney disease who was at least 18 years old. The online survey included questions regarding (1) informal caregiver's characteristics; (2) care recipient's characteristics; (3) intervention preferences (e.g. content, delivery format); and (4) informal caregiver's mental health. Informal caregiver's mental health was assessed using the 21 item Depression, Anxiety, and Stress Scale (DASS-21), which is composed of three subscales measuring depression, anxiety, and stress, respectively. Sixty-five individuals participated in the survey. See the published article for full study details. Description of uploaded files 1. ENTWINE_ESR14_Kidney Carer Survey Data_FULL_2022-08-30: Excel file with the complete, raw survey data. Note: the first half of participant's postal codes was collected, however this data was removed from the uploaded dataset to ensure participant anonymity. 2. ENTWINE_ESR14_Kidney Carer Survey Data_Clean DASS-21 Data_2022-08-30: Excel file with cleaned data for the DASS-21 scale. Data cleaning involved imputation of missing data if participants were missing data for one item within a subscale of the DASS-21. Missing values were imputed by finding the mean of all other items within the relevant subscale. 3. ENTWINE_ESR14_Kidney Carer Survey_KEY_2022-08-30: Excel file with key linking item labels in uploaded datasets with the corresponding survey question. 4. R Code for Kidney Carer Survey_2022-08-30: R file of R code used to analyse survey data. 5. R code for Kidney Carer Survey_PDF_2022-08-30: PDF file of R code used to analyse survey data.

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Description:

One of the leading retail stores in the US, Walmart, would like to predict the sales and demand accurately. There are certain events and holidays which impact sales on each day. There are sales data available for 45 stores of Walmart. The business is facing a challenge due to unforeseen demands and runs out of stock some times, due to the inappropriate machine learning algorithm. An ideal ML algorithm will predict demand accurately and ingest factors like economic conditions including CPI, Unemployment Index, etc.

Walmart runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of all, which are the Super Bowl, Labour Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks. Part of the challenge presented by this competition is modeling the effects of markdowns on these holiday weeks in the absence of complete/ideal historical data. Historical sales data for 45 Walmart stores located in different regions are available.

Acknowledgements

The dataset is taken from Kaggle.

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t single & multiple features.
• Also evaluate the models & compare their respective scores like R2, RMSE, etc.

This dataset provides a comprehensive collection of YouTube video and channel metadata curated for data analysis, visualization, and storytelling projects. It contains rich information on trending videos across multiple countries, including video performance statistics, engagement metrics, and channel-level details.

The dataset is designed to help learners and researchers explore real-world YouTube dynamics, such as: • What type of content gains the highest views and engagement? • How do categories perform across different countries? • What role do publishing time, video duration, or tags play in driving popularity? • Which channels dominate in terms of subscribers, views, and content consistency?

Features

The dataset includes detailed video-level fields such as: • Video ID, title, description, and publish time • Trending date and country • Tags, categories, duration, resolution, and licensed content status • Views, likes, and comment counts

Alongside channel-level information including: • Channel ID, title, and description • Channel country, publish date, and custom URL (if available) • Subscriber count, total views, video count, and hidden subscriber flag

With this structured dataset, students and professionals can perform data cleaning, transformation, SQL querying, trend analysis, and dashboarding in tools such as Excel, SQL, Power BI, Tableau, and Python. It is also suitable for advanced machine learning tasks like predicting video performance, engagement modeling, and natural language processing on video titles and descriptions.

Use Cases 1. Descriptive Analytics: Identify top categories, channels, and countries leading the YouTube trending space. 2. Comparative Analysis: Compare engagement rates across different regions and content types. 3. Visualization Projects: Create dashboards showing performance KPIs, category trends, and time-based patterns. 4. Storytelling: Derive business insights and best practices for creators, marketers, and educators on YouTube.

Educational Value

This dataset is structured specifically for student projects and group assignments. It ensures every learner can take a role—whether as a data engineer, analyst, visualization specialist, or business storyteller—mirroring the structure of real-world consulting projects.

Credits

This dataset is published as part of the YouTube Data Analytics Project initiated by Analytics Circle, an institute dedicated to empowering learners with practical data analytics, data science, and AI skills through hands-on projects and real-world applications.

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Description:

A simple yet challenging project, to predict the housing price based on certain factors like house area, bedrooms, furnished, nearness to mainroad, etc. The dataset is small yet, it's complexity arises due to the fact that it has strong multicollinearity. Can you overcome these obstacles & build a decent predictive model?

Acknowledgement:

Harrison, D. and Rubinfeld, D.L. (1978) Hedonic prices and the demand for clean air. J. Environ. Economics and Management 5, 81–102. Belsley D.A., Kuh, E. and Welsch, R.E. (1980) Regression Diagnostics. Identifying Influential Data and Sources of Collinearity. New York: Wiley.

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t a single & multiple feature.
• Also evaluate the models & compare thier respective scores like R2, RMSE, etc.

https://raw.githubusercontent.com/Masterx-AI/Project_Loan_Default_Risk_Expectancy_/main/loan.jpg" alt="">

Description:

Banks earn a major revenue from lending loans. But it is often associated with risk. The borrower's may default on the loan. To mitigate this issue, the banks have decided to use Machine Learning to overcome this issue. They have collected past data on the loan borrowers & would like you to develop a strong ML Model to classify if any new borrower is likely to default or not.

The dataset is enormous & consists of multiple deteministic factors like borrowe's income, gender, loan pupose etc. The dataset is subject to strong multicollinearity & empty values. Can you overcome these factors & build a strong classifier to predict defaulters?

Acknowledgements:

This dataset has been referred from Kaggle.

Objective:

• Understand the Dataset & cleanup (if required).
• Build classification model to predict weather the loan borrower will default or not.
• Also fine-tune the hyperparameters & compare the evaluation metrics of vaious classification algorithms.

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Description:

The advertising dataset captures the sales revenue generated with respect to advertisement costs across multiple channels like radio, tv, and newspapers.

It is required to understand the impact of ad budgets on the overall sales.

Acknowledgement:

The dataset is taken from Kaggle

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t a single & multiple features.
• Also evaluate the models & compare their respective scores like R2, RMSE, etc.