# "There Is No Such Thing as a Dumb Question," But There Are Good Ones

arXiv: https://arxiv.org/abs/2505.09923 · HTML: https://mlsanity.com/paper/2505.09923/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 45 references that are in the map.
- First posted on arXiv 15 May 2025.

## Builds on (most cited first, 13 of 13)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge](https://mlsanity.com/paper/2411.16594/) (2024)
- [FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets](https://mlsanity.com/paper/2307.10928/) (2023)
- [ProsocialDialog: A Prosocial Backbone for Conversational Agents](https://mlsanity.com/paper/2205.12688/) (2022)
- [Creativity: Generating Diverse Questions using Variational Autoencoders](https://mlsanity.com/paper/1704.03493/) (2017)
- [Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations](https://mlsanity.com/paper/2004.14530/) (2020)
- [Question Asking as Program Generation](https://mlsanity.com/paper/1711.06351/) (2017)
- [SQuARe: A Large-Scale Dataset of Sensitive Questions and Acceptable Responses Created Through Human-Machine Collaboration](https://mlsanity.com/paper/2305.17696/) (2023)
- [QAScore -- An Unsupervised Unreferenced Metric for the Question Generation Evaluation](https://mlsanity.com/paper/2210.04320/) (2022)
- [Evaluation of Question Generation Needs More References](https://mlsanity.com/paper/2305.16626/) (2023)
- [Diversity Enhanced Narrative Question Generation for Storybooks](https://mlsanity.com/paper/2310.16446/) (2023)
- [CAUS: A Dataset for Question Generation based on Human Cognition Leveraging Large Language Models](https://mlsanity.com/paper/2404.11835/) (2024)

## Abstract (arXiv)

Questioning has become increasingly crucial for both humans and artificial intelligence, yet there remains limited research comprehensively assessing question quality. In response, this study defines good questions and presents a systematic evaluation framework. We propose two key evaluation dimensions: appropriateness (sociolinguistic competence in context) and effectiveness (strategic competence in goal achievement). Based on these foundational dimensions, a rubric-based scoring system was developed. By incorporating dynamic contextual variables, our evaluation framework achieves structure and flexibility through semi-adaptive criteria. The methodology was validated using the CAUS and SQUARE datasets, demonstrating the ability of the framework to access both well-formed and problematic questions while adapting to varied contexts. As we establish a flexible and comprehensive framework for question evaluation, this study takes a significant step toward integrating questioning behavior with structured analytical methods grounded in the intrinsic nature of questioning.