# Unanswerable Questions about Images and Texts

arXiv: https://arxiv.org/abs/2102.06793 · HTML: https://mlsanity.com/paper/2102.06793/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 29 references that are in the map.
- First posted on arXiv 25 Jan 2021.

## Builds on (most cited first, 16 of 16)

- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions](https://mlsanity.com/paper/1905.10044/) (2019)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [VizWiz Grand Challenge: Answering Visual Questions from Blind People](https://mlsanity.com/paper/1802.08218/) (2018)
- [Adversarial Examples for Evaluating Reading Comprehension Systems](https://mlsanity.com/paper/1707.07328/) (2017)
- [Deep Learning for Symbolic Mathematics](https://mlsanity.com/paper/1912.01412/) (2019)
- [Probing Neural Network Comprehension of Natural Language Arguments](https://mlsanity.com/paper/1907.07355/) (2019)
- [Why Does a Visual Question Have Different Answers?](https://mlsanity.com/paper/1908.04342/) (2019)
- [Read + Verify: Machine Reading Comprehension with Unanswerable Questions](https://mlsanity.com/paper/1808.05759/) (2018)
- [Challenges and Prospects in Vision and Language Research](https://mlsanity.com/paper/1904.09317/) (2019)
- [Question Relevance in VQA: Identifying Non-Visual And False-Premise Questions](https://mlsanity.com/paper/1606.06622/) (2016)
- [The Promise of Premise: Harnessing Question Premises in Visual Question Answering](https://mlsanity.com/paper/1705.00601/) (2017)
- [The Use of Deep Learning for Symbolic Integration: A Review of (Lample and Charton, 2019)](https://mlsanity.com/paper/1912.05752/) (2019)
- [Learning to Ask Unanswerable Questions for Machine Reading Comprehension](https://mlsanity.com/paper/1906.06045/) (2019)
- [U-Net: Machine Reading Comprehension with Unanswerable Questions](https://mlsanity.com/paper/1810.06638/) (2018)

## Built on by (most cited first, 7 of 7)

- [Reliable Visual Question Answering: Abstain Rather Than Answer Incorrectly](https://mlsanity.com/paper/2204.13631/) (2022)
- [Won't Get Fooled Again: Answering Questions with False Premises](https://mlsanity.com/paper/2307.02394/) (2023)
- [See, Say, and Segment: Teaching LMMs to Overcome False Premises](https://mlsanity.com/paper/2312.08366/) (2023)
- [Certainly Uncertain: A Benchmark and Metric for Multimodal Epistemic and Aleatoric Awareness](https://mlsanity.com/paper/2407.01942/) (2024)
- [Unsolvable Problem Detection: Robust Understanding Evaluation for Large Multimodal Models](https://mlsanity.com/paper/2403.20331/) (2024)
- [Asking More Informative Questions for Grounded Retrieval](https://mlsanity.com/paper/2311.08584/) (2023)
- [SimpsonsVQA: Enhancing Inquiry-Based Learning with a Tailored Dataset](https://mlsanity.com/paper/2410.22648/) (2024)

## Abstract (arXiv)

Questions about a text or an image that cannot be answered raise distinctive issues for an AI. This note discusses the problem of unanswerable questions in VQA (visual question answering), in QA (visual question answering), and in AI generally.