# Question Answering is a Format; When is it Useful?

arXiv: https://arxiv.org/abs/1909.11291 · HTML: https://mlsanity.com/paper/1909.11291/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2019.

## Built on by (most cited first, 25 of 25)

- [QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension](https://mlsanity.com/paper/2107.12708/) (2021)
- [Few-Shot Self-Rationalization with Natural Language Prompts](https://mlsanity.com/paper/2111.08284/) (2021)
- [SpartQA: : A Textual Question Answering Benchmark for Spatial Reasoning](https://mlsanity.com/paper/2104.05832/) (2021)
- [CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation](https://mlsanity.com/paper/2211.00295/) (2022)
- [FaVIQ: FAct Verification from Information-seeking Questions](https://mlsanity.com/paper/2107.02153/) (2021)
- [It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performance](https://mlsanity.com/paper/2305.09022/) (2023)
- [Question Answering Infused Pre-training of General-Purpose Contextualized Representations](https://mlsanity.com/paper/2106.08190/) (2021)
- [Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets](https://mlsanity.com/paper/2302.13959/) (2023)
- [Complex Factoid Question Answering with a Free-Text Knowledge Graph](https://mlsanity.com/paper/2103.12876/) (2021)
- [Dependency Parsing as MRC-based Span-Span Prediction](https://mlsanity.com/paper/2105.07654/) (2021)
- [Hey AI, Can You Solve Complex Tasks by Talking to Agents?](https://mlsanity.com/paper/2110.08542/) (2021)
- [Calibrating Trust of Multi-Hop Question Answering Systems with Decompositional Probes](https://mlsanity.com/paper/2204.07693/) (2022)
- [RadLing: Towards Efficient Radiology Report Understanding](https://mlsanity.com/paper/2306.02492/) (2023)
- [ProMQA-Assembly: Multimodal Procedural QA Dataset on Assembly](https://mlsanity.com/paper/2509.02949/) (2025)
- [PInKS: Preconditioned Commonsense Inference with Minimal Supervision](https://mlsanity.com/paper/2206.07920/) (2022)
- [Promptly Predicting Structures: The Return of Inference](https://mlsanity.com/paper/2401.06877/) (2024)
- [Improving Information Extraction on Business Documents with Specific Pre-Training Tasks](https://mlsanity.com/paper/2309.05429/) (2023)
- [On Event Individuation for Document-Level Information Extraction](https://mlsanity.com/paper/2212.09702/) (2022)
- [The Case for Scalable, Data-Driven Theory: A Paradigm for Scientific Progress in NLP](https://mlsanity.com/paper/2312.00349/) (2023)
- [Mapping the Course for Prompt-based Structured Prediction](https://mlsanity.com/paper/2508.15090/) (2025)
- [Assessing biomedical knowledge robustness in large language models by query-efficient sampling attacks](https://mlsanity.com/paper/2402.10527/) (2024)
- [A Systematic Survey of Natural Language Processing for the Greek Language](https://mlsanity.com/paper/2407.09861/) (2024)
- [Is Sluice Resolution really just Question Answering?](https://mlsanity.com/paper/2105.14347/) (2021)
- [HealthLoopQA: A Context-Aware Question Answering Benchmark for Interpreting Wearable Monitoring Data in Diabetes Care](https://mlsanity.com/paper/2609.06976/) (2026)
- [Emergent Unfaithfulness: How Alignment Training Causes Language Models to Silently Override Task Faithfulness](https://mlsanity.com/paper/2610.00568/) (2026)

## Abstract (arXiv)

Recent years have seen a dramatic expansion of tasks and datasets posed as question answering, from reading comprehension, semantic role labeling, and even machine translation, to image and video understanding. With this expansion, there are many differing views on the utility and definition of "question answering" itself. Some argue that its scope should be narrow, or broad, or that it is overused in datasets today. In this opinion piece, we argue that question answering should be considered a format which is sometimes useful for studying particular phenomena, not a phenomenon or task in itself. We discuss when a task is correctly described as question answering, and when a task is usefully posed as question answering, instead of using some other format.