title: PlaSma: Making Small Language Models Better Procedural Knowledge Models
    for (Counterfactual) Planning
summary: Procedural planning, which entails decomposing a high-level goal into a
  sequence of temporally ordered steps, is an important yet intricate task for
  machines. It involves integrating common-sense knowledge to reason about
  complex contextualized situations that are often counterfactual, e.g.
  "scheduling a doctor's appointment without a phone". While current approaches
  show encouraging results using large language models (LLMs), they are hindered
  by drawbacks such as costly API calls and reproducibility issues. In this
  paper, we advocate planning using smaller language models. We present PlaSma, a
  novel two-pronged approach to endow small language models with procedural
  knowledge and (counterfactual) planning capabilities. More concretely, we
  develop symbolic procedural knowledge distillation to enhance the implicit
  knowledge in small language models and an inference-time algorithm to
  facilitate more structured and accurate reasoning. In addition, we introduce a
  novel task, Counterfactual Planning, that requires a revision of a plan to cope
  with a counterfactual situation. In both the original and counterfactual
  setting, we show that orders-of-magnitude smaller models (770M-11B parameters)
  can compete and often surpass their larger teacher models' capabilities.
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paper_authors.name: Faeze Brahman
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paper_authors.fullname: fa brah
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paper_authors.name: Chandra Bhagavatula
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paper_authors.name: Valentina Pyatkin
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paper_authors.fullname: Valentina Pyatkin
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paper_authors.name: Jena D. Hwang
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paper_authors.fullname: jena hwang
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paper_authors._id: 6477fb018ab7e732b6da56a1
paper_authors.name: Xiang Lorraine Li
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paper_authors.fullname: Xiang Lorraine Li
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paper_authors.user: lorraine921
paper_authors._id: 6477fb018ab7e732b6da56a2
paper_authors.name: Hirona J. Arai
paper_authors._id: 6477fb018ab7e732b6da56a3
paper_authors.name: Soumya Sanyal
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paper_authors.fullname: Soumya Sanyal
paper_authors.type: user
paper_authors.user: soumyasanyal
paper_authors._id: 6477fb018ab7e732b6da56a4
paper_authors.name: Keisuke Sakaguchi
paper_authors._id: 6477fb018ab7e732b6da56a5
paper_authors.name: Xiang Ren
paper_authors._id: 6477fb018ab7e732b6da56a6
paper_authors.name: Yejin Choi
paper_title: PlaSma: Making Small Language Models Better Procedural Knowledge Models
    for (Counterfactual) Planning
paper_summary: Procedural planning, which entails decomposing a high-level goal into a
  sequence of temporally ordered steps, is an important yet intricate task for
  machines. It involves integrating common-sense knowledge to reason about
  complex contextualized situations that are often counterfactual, e.g.
  "scheduling a doctor's appointment without a phone". While current approaches
  show encouraging results using large language models (LLMs), they are hindered
  by drawbacks such as costly API calls and reproducibility issues. In this
  paper, we advocate planning using smaller language models. We present PlaSma, a
  novel two-pronged approach to endow small language models with procedural
  knowledge and (counterfactual) planning capabilities. More concretely, we
  develop symbolic procedural knowledge distillation to enhance the implicit
  knowledge in small language models and an inference-time algorithm to
  facilitate more structured and accurate reasoning. In addition, we introduce a
  novel task, Counterfactual Planning, that requires a revision of a plan to cope
  with a counterfactual situation. In both the original and counterfactual
  setting, we show that orders-of-magnitude smaller models (770M-11B parameters)
  can compete and often surpass their larger teacher models' capabilities.
paper_ai_summary: PlaSma enhances small language models with procedural knowledge and planning capabilities through symbolic distillation and an inference algorithm, demonstrating competitive performance with larger models.
paper_ai_keywords: procedural planning large language models procedural knowledge knowledge distillation procedural knowledge distillation inference-time algorithm symbolic procedural knowledge counterfactual planning small language models