# CAUS: A Dataset for Question Generation based on Human Cognition Leveraging Large Language Models

arXiv: https://arxiv.org/abs/2404.11835 · HTML: https://mlsanity.com/paper/2404.11835/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 42 references that are in the map.
- First posted on arXiv 18 Apr 2024.

## Builds on (most cited first, 12 of 12)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Bias and Fairness in Large Language Models: A Survey](https://mlsanity.com/paper/2309.00770/) (2023)
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://mlsanity.com/paper/2102.07350/) (2021)
- [Do Prompt-Based Models Really Understand the Meaning of their Prompts?](https://mlsanity.com/paper/2109.01247/) (2021)
- [The Generative AI Paradox: "What It Can Create, It May Not Understand"](https://mlsanity.com/paper/2311.00059/) (2023)
- [Can ChatGPT Defend its Belief in Truth? Evaluating LLM Reasoning via Debate](https://mlsanity.com/paper/2305.13160/) (2023)
- [Learning by Asking Questions](https://mlsanity.com/paper/1712.01238/) (2017)
- [Creativity: Generating Diverse Questions using Variational Autoencoders](https://mlsanity.com/paper/1704.03493/) (2017)
- [Question-type Driven Question Generation](https://mlsanity.com/paper/1909.00140/) (2019)
- [Question Asking as Program Generation](https://mlsanity.com/paper/1711.06351/) (2017)
- [Diversity Enhanced Narrative Question Generation for Storybooks](https://mlsanity.com/paper/2310.16446/) (2023)

## Built on by (most cited first, 1 of 1)

- ["There Is No Such Thing as a Dumb Question," But There Are Good Ones](https://mlsanity.com/paper/2505.09923/) (2025)

## Abstract (arXiv)

We introduce the Curious About Uncertain Scene (CAUS) dataset, designed to enable Large Language Models, specifically GPT-4, to emulate human cognitive processes for resolving uncertainties. Leveraging this dataset, we investigate the potential of LLMs to engage in questioning effectively. Our approach involves providing scene descriptions embedded with uncertainties to stimulate the generation of reasoning and queries. The queries are then classified according to multi-dimensional criteria. All procedures are facilitated by a collaborative system involving both LLMs and human researchers. Our results demonstrate that GPT-4 can effectively generate pertinent questions and grasp their nuances, particularly when given appropriate context and instructions. The study suggests that incorporating human-like questioning into AI models improves their ability to manage uncertainties, paving the way for future advancements in Artificial Intelligence (AI).