Datasheet: STAQ (Synthetic Technology Assistance Queries)
This datasheet follows the "Datasheets for Datasets" framework (Gebru et al., 2021). For dataset contents and usage, see the README; for the language variety and population framing, see the data statement.
Motivation
For what purpose was the dataset created?
STAQ was created to support building age-inclusive artificial intelligence (AI) systems. Large-scale training data underrepresents older adults, and asking older adults to generate the volume of data needed to train or fine-tune foundation models is impractical and ethically burdensome. STAQ provides a scalable, synthetic bridge for model training and evaluation, grounded in real observed behavior rather than stereotypes. It is meant to complement, not replace, participatory design and human evaluation.
Who created the dataset?
The dataset was created by Hasti Sharifi, Homaira Huda Shomee, Melissa Lamar, Sourav Medya, and Debaleena Chattopadhyay, in connection with the ASSETS '26 paper "Helping the Helper: LLM-Assisted Problem Articulation for Older Adults Seeking Technology Support."
Who funded the creation of the dataset?
This work was supported in part by the National Institute on Aging of the National Institutes of Health under Award No. P30AG083255. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Composition
What do the instances represent?
Each instance is a technology help-seeking query written in the communication style of older adults, paired with a paraphrase that clarifies the intended meaning, a communication-characteristic label, and (for a sampled subset) an expert plausibility rating.
How many instances are there?
There are 514 instances, distributed across four categories, as shown below:
| Category | Count |
|---|---|
| under-specification | 154 |
| verbosity | 120 |
| over-specification | 120 |
| incompleteness | 120 |
Is the dataset a sample, or does it contain all instances?
The dataset is a generated set, not a sample of a larger fixed collection. It was produced to cover the four target communication characteristics; the counts above reflect the released composition.
What data does each instance consist of?
Each row has five fields: id (unique identifier), query (older-adult-style
query), rephrased_query (AI-generated paraphrase), category (one of the four
characteristics), and expert_rating (expert plausibility rating).
Is there a label or target associated with each instance?
Yes. category labels every instance. rephrased_query serves as a paired
reference/target for paraphrasing tasks. expert_rating is an additional label
available only for the evaluated subset.
Is any information missing from individual instances?
Yes. expert_rating is populated only for the 50-query face-validity sample; the
remaining rows are unrated. Missing ratings should not be read as low ratings.
Are relationships between instances made explicit?
Each query is explicitly paired with its rephrased_query, and grouped by
category. There are no other cross-instance relationships.
Are there recommended data splits?
No predefined train/validation/test splits are provided; the dataset is a single
flat file. Users may split it as appropriate, ideally stratified by category.
Are there errors, sources of noise, or redundancies?
Because the data is model-generated, some queries are less realistic than others: in the face-validity review, 9 of 50 sampled queries were rated "unlikely" and 1 "possibly." Users should expect some variation in realism.
Is the dataset self-contained?
Yes. The released dataset is a single self-contained CSV file. It was grounded in a private diary-study corpus (see Collection process), which is not included.
Does the dataset contain confidential or sensitive data?
No. STAQ is synthetic and contains no real personal information. Scenarios reference common platforms and apps (for example, email, video calls, ride-hailing) but describe fictional situations and no real individuals. The dataset concerns a population defined by age but contains no real individuals' data.
Collection process
How was the data acquired?
The queries were generated by a large language model (GPT-4o) using few-shot prompting, anchored in real, unstructured technology-support queries from a formative diary study with older adults.
What mechanisms or procedures were used?
Generation used few-shot prompting: each prompt contained three example pairs (an original older-adult-style query and its expert paraphrase) and instructed the model to produce one new example in the same conversational style, output as JSON. The model was directed to vary the technical issues and was not asked to emulate a generic "older adult." The full prompt appears in the paper's appendix.
Who was involved in the collection process?
The research team designed the prompts and generation procedure. The released
rephrased_query paraphrases were generated by the model (GPT-4o); domain
experts in aging and technology wrote the paraphrases used as few-shot examples
in the prompt and later performed the face-validity review.
Over what timeframe was the data collected?
The synthetic queries were generated in 2025. The underlying diary study was conducted earlier; see the paper, Section 3.
Were any ethical review processes conducted?
Yes. All study procedures for the underlying diary study were approved by the Institutional Review Board (IRB) of the University of Illinois Chicago. STAQ itself is synthetic and derived from that IRB-approved study.
Does the dataset relate to people?
Indirectly. STAQ contains no real individuals' data, but it is designed to represent the communication of older adults and was grounded in data collected from older-adult participants under IRB approval.
Preprocessing, cleaning, and labeling
Was any preprocessing, cleaning, or labeling done?
Communication-characteristic labels were assigned as part of generation. The generated text was analyzed for token length and lexical diversity (type-token ratio) to validate realism against the real data. For any additional filtering or curation steps, see the paper.
Was the raw data saved in addition to the processed data?
The real diary-study queries that seeded generation are kept private to protect participant privacy and are not released. The released STAQ file is the distributed artifact.
What software or models were used?
GPT-4o was used for generation. A Sentence-BERT (SBERT) model with Principal Component Analysis (PCA) was used to validate semantic fidelity against the real data.
Uses
Has the dataset been used for any tasks already?
No; STAQ is released as a resource and was not used for a downstream task in the paper.
What other tasks could the dataset be used for?
Potential uses include training or evaluating age-inclusive query understanding, paraphrasing, and clarification systems, and research on accessible technology support for older adults.
Is there anything about the composition or collection that might affect future uses?
Yes. The data is synthetic (en-US) and grounded in one diary study; it may not
generalize across languages, cultures, or the full diversity of older adults.
expert_rating covers only the 50-query sample. Users should account for these
limits and validate against real data where possible.
Are there tasks for which the dataset should not be used?
STAQ should not be treated as a substitute for real older adults' behavior, used to make claims about real individuals or subgroups, or used to replace human evaluation and participatory research.
Distribution
How will the dataset be distributed?
The canonical copy is archived on Zenodo with a Digital Object Identifier (DOI),
with a mirror on Hugging Face (hhshomee2/STAQ) for discoverability.
Under what license?
The dataset is released under the Creative Commons Attribution 4.0 International license (CC BY 4.0): free to share and adapt with attribution.
Are there IP-based or other restrictions?
No restrictions beyond the attribution requirement of CC BY 4.0. No third-party IP, export controls, or usage fees apply.
Maintenance
Who maintains the dataset, and how can they be contacted?
The dataset is maintained by the authors. See the README for the current contact and citation details.
Will the dataset be updated, and how will updates be communicated?
Updates and corrections will be released as new versions on Zenodo, each with its own version DOI under a shared concept DOI, so earlier versions remain citable and accessible.
Version [1.0.0] — [date]. Datasheet schema: Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., & Crawford, K. (2021). Datasheets for Datasets. Communications of the ACM, 64(12), 86–92.