UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations
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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Wenting Zhao
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Wenting Zhao
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Justin T Chiu
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Justin T Chiu
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Jena D. Hwang
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jena hwang
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Faeze Brahman
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Faeze Brahman
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Jack Hessel
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Jack Hessel
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Sanjiban Choudhury
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Sanjiban Choudhury
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Yejin Choi
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Yejin Choi
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Xiang Lorraine Li
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Xiang Lorraine Li
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Alane Suhr
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Alane Suhr
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UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations
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.
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.
uncommonsense abductive reasoning UNcommonsense abductive reasoning commonsense abductive reasoning online imitation learning large language models supervised fine-tuning