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A prominent challenge for modern language understanding systems is theability to answer implicit reasoning questions, where the required reasoningsteps for answering the question are not mentioned in the text explicitly. Inthis work, we investigate why current models struggle with implicit reasoningquestion answering (QA) tasks, by decoupling inference of reasoning steps fromtheir execution. We define a new task of implicit relation inference andconstruct a benchmark, IMPLICITRELATIONS, where given a question, a modelshould output a list of concept-relation pairs, where the relations describethe implicit reasoning steps required for answering the question. UsingIMPLICITRELATIONS, we evaluate models from the GPT-3 family and find that,while these models struggle on the implicit reasoning QA task, they oftensucceed at inferring implicit relations. This suggests that the challenge inimplicit reasoning questions does not stem from the need to plan a reasoningstrategy alone, but to do it while also retrieving and reasoning over relevantinformation.

Inferring Implicit Relations in Complex Questions with Language Models