title: UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations
summary: Language technologies that accurately model the dynamics of events must
  perform commonsense reasoning. Existing work evaluating commonsense reasoning
  focuses on making inferences about common, everyday situations. To instead
  investigate the ability to model unusual, unexpected, and unlikely situations,
  we explore the task of uncommonsense abductive reasoning. Given a piece of
  context with an unexpected outcome, this task requires reasoning abductively to
  generate a natural language explanation that makes the unexpected outcome more
  likely in the context. To this end, we curate and release a new English
  language corpus called UNcommonsense. We characterize the differences between
  the performance of human explainers and the best performing large language
  models, finding that model-enhanced human-written explanations achieve the
  highest quality by trading off between specificity and diversity. Finally, we
  experiment with several online imitation learning algorithms to train open and
  accessible language models on this task. When compared with the vanilla
  supervised fine-tuning approach, these methods consistently reduce lose rates
  on both common and uncommonsense abductive reasoning judged by human
  evaluators.
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paper_authors.name: Sanjiban Choudhury
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paper_authors.name: Yejin Choi
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paper_authors.name: Xiang Lorraine Li
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paper_title: UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations
paper_summary: Language technologies that accurately model the dynamics of events must
  perform commonsense reasoning. Existing work evaluating commonsense reasoning
  focuses on making inferences about common, everyday situations. To instead
  investigate the ability to model unusual, unexpected, and unlikely situations,
  we explore the task of uncommonsense abductive reasoning. Given a piece of
  context with an unexpected outcome, this task requires reasoning abductively to
  generate a natural language explanation that makes the unexpected outcome more
  likely in the context. To this end, we curate and release a new English
  language corpus called UNcommonsense. We characterize the differences between
  the performance of human explainers and the best performing large language
  models, finding that model-enhanced human-written explanations achieve the
  highest quality by trading off between specificity and diversity. Finally, we
  experiment with several online imitation learning algorithms to train open and
  accessible language models on this task. When compared with the vanilla
  supervised fine-tuning approach, these methods consistently reduce lose rates
  on both common and uncommonsense abductive reasoning judged by human
  evaluators.
paper_ai_summary: Research focuses on uncommonsense abductive reasoning using a new corpus to improve model explanations of unexpected outcomes, showing that model-augmented human explanations and online imitation learning enhance performance.
paper_ai_keywords: uncommonsense abductive reasoning UNcommonsense abductive reasoning commonsense abductive reasoning online imitation learning large language models supervised fine-tuning