# For those who don't know (how) to ask: Building a dataset of technology questions for digital newcomers

arXiv: https://arxiv.org/abs/2403.18125 · HTML: https://mlsanity.com/paper/2403.18125/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 10 references that are in the map.
- First posted on arXiv 26 Mar 2024.

## Builds on (most cited first, 1 of 1)

- [Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity](https://mlsanity.com/paper/2310.07521/) (2023)

## Built on by (most cited first, 2 of 2)

- [Supporting the Digital Autonomy of Elders Through LLM Assistance](https://mlsanity.com/paper/2407.15695/) (2024)
- [What Would You Ask When You First Saw a2+b2=c2? Evaluating LLM on Curiosity-Driven Questioning](https://mlsanity.com/paper/2409.17172/) (2024)

## Abstract (arXiv)

While the rise of large language models (LLMs) has created rich new opportunities to learn about digital technology, many on the margins of this technology struggle to gain and maintain competency due to lexical or conceptual barriers that prevent them from asking appropriate questions. Although there have been many efforts to understand factuality of LLM-created content and ability of LLMs to answer questions, it is not well understood how unclear or nonstandard language queries affect the model outputs. We propose the creation of a dataset that captures questions of digital newcomers and outsiders, utilizing data we have compiled from a decade's worth of one-on-one tutoring. In this paper we lay out our planned efforts and some potential uses of this dataset.