mlsanityData to 14 Oct 2026

CURIE: An Iterative Querying Approach for Reasoning About Situations

arXiv 2104.00814 ↗First posted 1 Apr 2021cs.CLcited by 3

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Abstract (arXiv)

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

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 25 of its 45 references that are in the map. First posted on arXiv 1 Apr 2021.

Builds on

25 of 45 references.

References · 25

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13 more
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Citing papers per year: 2022: 2; 2023: 1.

3 papers.

  1. 2022Language Models of Code are Few-Shot Commonsense Learnerscited by 160
  2. 2023Explanation Graph Generation via Generative Pre-training over Synthetic Graphscited by 1
  3. 2022elBERto: Self-supervised Commonsense Learning for Question Answeringcited by 0