# Question Asking as Program Generation

arXiv: https://arxiv.org/abs/1711.06351 · HTML: https://mlsanity.com/paper/1711.06351/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2017.

## Built on by (most cited first, 13 of 13)

- [Modeling rapid language learning by distilling Bayesian priors into artificial neural networks](https://mlsanity.com/paper/2305.14701/) (2023)
- [Open-domain clarification question generation without question examples](https://mlsanity.com/paper/2110.09779/) (2021)
- [CLARINET: Augmenting Language Models to Ask Clarification Questions for Retrieval](https://mlsanity.com/paper/2405.15784/) (2024)
- [Goals as Reward-Producing Programs](https://mlsanity.com/paper/2405.13242/) (2024)
- [Loose LIPS Sink Ships: Asking Questions in Battleship with Language-Informed Program Sampling](https://mlsanity.com/paper/2402.19471/) (2024)
- [Shoot First, Ask Questions Later? Building Rational Agents that Explore and Act Like People](https://mlsanity.com/paper/2510.20886/) (2025)
- [Simple or Complex? Complexity-Controllable Question Generation with Soft Templates and Deep Mixture of Experts Model](https://mlsanity.com/paper/2110.06560/) (2021)
- [DASH: Modularized Human Manipulation Simulation with Vision and Language for Embodied AI](https://mlsanity.com/paper/2108.12536/) (2021)
- [CAUS: A Dataset for Question Generation based on Human Cognition Leveraging Large Language Models](https://mlsanity.com/paper/2404.11835/) (2024)
- [Integrating Neural and Symbolic Components in a Model of Pragmatic Question-Answering](https://mlsanity.com/paper/2506.01474/) (2025)
- [A Computational Cognitive Model for Processing Repetitions of Hierarchical Relations](https://mlsanity.com/paper/2504.10065/) (2025)
- ["There Is No Such Thing as a Dumb Question," But There Are Good Ones](https://mlsanity.com/paper/2505.09923/) (2025)
- [CLAIM: Leading Open-domain Active Clarification of Large Language Models with Uncertainty Measurement](https://mlsanity.com/paper/2608.11631/) (2026)

## Abstract (arXiv)

A hallmark of human intelligence is the ability to ask rich, creative, and revealing questions. Here we introduce a cognitive model capable of constructing human-like questions. Our approach treats questions as formal programs that, when executed on the state of the world, output an answer. The model specifies a probability distribution over a complex, compositional space of programs, favoring concise programs that help the agent learn in the current context. We evaluate our approach by modeling the types of open-ended questions generated by humans who were attempting to learn about an ambiguous situation in a game. We find that our model predicts what questions people will ask, and can creatively produce novel questions that were not present in the training set. In addition, we compare a number of model variants, finding that both question informativeness and complexity are important for producing human-like questions.