What Does My QA Model Know? Devising Controlled Probes Using Expert Knowledge

What Does My QA Model Know? Devising Controlled Probes Using Expert Knowledge Kyle Richardson and Ashish Sabharwal Allen Institute for AI, Seattle, WA, USA {kyler,ashishs@allenai.org} Abstract Open-domainquestionanswering(QA) involves many knowledge and reasoning challenges, but are successful QA models actually learning such knowledge when trained on benchmark QA tasks? We investigate this via several new diagnostic tasks probing whether multiple- choice QA models know definitions and taxo- nomic reasoning—two skills widespread in existing benchmarks and fundamental to more complex reasoning. We introduce a methodo- logy for automatically building probe datasets from expert knowledge sources, allowing for systematic control and a comprehensive evalua- tion. We include ways to carefully control for artifacts that may arise during this process. Our evaluation confirms that transformer- based multiple-choice QA models are already predisposed to recognize certain types of structural linguistic knowledge. However, it also reveals a more nuanced picture: their performance notably degrades even with a slight increase in the number of ‘‘hops’’ in the underlying taxonomic hierarchy, and with more challenging distractor candidates. Fur- ther, existing models are far from perfect when assessed at the level of clusters of seman- tically connected probes, such as all hypernym questions about a single concept. 1 Introduction Automatically answering questions, especially in the open-domain setting where minimal or no contextual knowledge is explicitly provided, requires considerable background knowledge and reasoning abilities. For example, answering the two questions in the top gray box in Figure 1 requires identifying a specific ISA relation (that ‘cooking’ is a type of ‘learned behavior’) as well as recalling a concept definition (that ‘global warming’ is defined as a ‘worldwide increase in temperature’). Recent success in QA has been driven largely by new benchmarks (Zellers et al., 2018; Talmor et al., 2019b; Bhagavatula et al., 2020; Khot et al., 2020) and advances in model pre-training (Radford et al., 2018; Devlin et al., 2019). This raises a natural question: Do state-of-the-art multiple-choice QA (MCQA) models that excel at standard benchmarks truly possess basic know- ledge and reasoning skills expected in these tasks? Answering this question is challenging because of limited understanding of heavily pre-trained complex models and the way existing MCQA datasets are constructed. We focus on the second aspect, which has two limitations: Large-scale crowdsourcing leaves little systematic control over question semantics or requisite background knowledge (Welbl et al., 2017), while questions from real exams tend to mix multiple challenges in a single dataset, often even in a single question (Clark et al., 2018; Boratko et al., 2018). To address this challenge, we propose systema- tically constructing model competence probes by exploiting structured information contained in expert knowledge sources such as knowledge graphs and lexical taxonomies. Importantly, these probes are diagnostic tasks, designed not to impart new knowledge but to assess what models trained on standard QA benchmarks already know; as such, they serve as proxies for the types of questions that a model might encounter in its original task, but involve a single category of knowledge under various controlled conditions and perturbations. …

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