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Recently, models have been shown to predict the effects of unexpectedsituations, e.g., would cloudy skies help or hinder plant growth? Given acontext, the goal of such situational reasoning is to elicit the consequencesof a new situation (st) that arises in that context. We propose a method toiteratively build a graph of relevant consequences explicitly in a structuredsituational graph (st-graph) using natural language queries over a finetunedlanguage model (M). Across multiple domains, CURIE generates st-graphs thathumans find relevant and meaningful in eliciting the consequences of a newsituation. We show that st-graphs generated by CURIE improve a situationalreasoning end task (WIQA-QA) by 3 points on accuracy by simply augmenting theirinput with our generated situational graphs, especially for a hard subset thatrequires background knowledge and multi-hop reasoning.

CURIE: An Iterative Querying Approach for Reasoning About Situations