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Although counterfactual reasoning is a fundamental aspect of intelligence,the lack of large-scale counterfactual open-domain question-answering (QA)benchmarks makes it difficult to evaluate and improve models on this ability.To address this void, we introduce the first such dataset, named IfQA, whereeach question is based on a counterfactual presupposition via an "if" clause.For example, if Los Angeles was on the east coast of the U.S., what would bethe time difference between Los Angeles and Paris? Such questions requiremodels to go beyond retrieving direct factual knowledge from the Web: they mustidentify the right information to retrieve and reason about an imaginedsituation that may even go against the facts built into their parameters. TheIfQA dataset contains over 3,800 questions that were annotated annotated bycrowdworkers on relevant Wikipedia passages. Empirical analysis reveals thatthe IfQA dataset is highly challenging for existing open-domain QA methods,including supervised retrieve-then-read pipeline methods (EM score 36.2), aswell as recent few-shot approaches such as chain-of-thought prompting withGPT-3 (EM score 27.4). The unique challenges posed by the IfQA benchmark willpush open-domain QA research on both retrieval and counterfactual reasoningfronts.

IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions