title: Certified Reasoning with Language Models
summary: Language models often achieve higher accuracy when reasoning step-by-step in
  complex tasks. However, their reasoning can be unsound, inconsistent, or rely
  on undesirable prior assumptions. To tackle these issues, we introduce a class
  of tools for language models called guides that use state and incremental
  constraints to guide generation. A guide can be invoked by the model to
  constrain its own generation to a set of valid statements given by the tool. In
  turn, the model's choices can change the guide's state. We show how a general
  system for logical reasoning can be used as a guide, which we call LogicGuide.
  Given a reasoning problem in natural language, a model can formalize its
  assumptions for LogicGuide and then guarantee that its reasoning steps are
  sound. In experiments with the PrOntoQA and ProofWriter reasoning datasets,
  LogicGuide significantly improves the performance of GPT-3, GPT-3.5 Turbo and
  LLaMA (accuracy gains up to 35%). LogicGuide also drastically reduces content
  effects: the interference of prior and current assumptions that both humans and
  language models have been shown to suffer from. Finally, we explore
  bootstrapping LLaMA 13B from its own reasoning and find that LogicGuide is
  critical: by training only on certified self-generated reasoning, LLaMA can
  self-improve, avoiding learning from its own hallucinations.
paper_authors._id: 64812cebc0d3c70316d54fce
paper_authors.name: Gabriel Poesia
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paper_authors.fullname: Gabriel Poesia
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paper_authors.user: gpoesia
paper_authors._id: 64812cebc0d3c70316d54fcf
paper_authors.name: Kanishk Gandhi
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paper_authors.fullname: Kanishk Gandhi
paper_authors.type: user
paper_authors.user: obiwan96
paper_authors._id: 64812cebc0d3c70316d54fd0
paper_authors.name: Eric Zelikman
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paper_authors.fullname: Eric Zelikman
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paper_authors.user: ezelikman
paper_authors._id: 64812cebc0d3c70316d54fd1
paper_authors.name: Noah D. Goodman
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-15T09:56:22.538Z
paper_authors._id: 67321274c1f20c742bcf7a8d
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paper_authors.fullname: Noah D. Goodman
paper_authors.type: user
paper_authors.user: ngoodman
paper_title: Certified Reasoning with Language Models
paper_summary: Language models often achieve higher accuracy when reasoning step-by-step in
  complex tasks. However, their reasoning can be unsound, inconsistent, or rely
  on undesirable prior assumptions. To tackle these issues, we introduce a class
  of tools for language models called guides that use state and incremental
  constraints to guide generation. A guide can be invoked by the model to
  constrain its own generation to a set of valid statements given by the tool. In
  turn, the model's choices can change the guide's state. We show how a general
  system for logical reasoning can be used as a guide, which we call LogicGuide.
  Given a reasoning problem in natural language, a model can formalize its
  assumptions for LogicGuide and then guarantee that its reasoning steps are
  sound. In experiments with the PrOntoQA and ProofWriter reasoning datasets,
  LogicGuide significantly improves the performance of GPT-3, GPT-3.5 Turbo and
  LLaMA (accuracy gains up to 35%). LogicGuide also drastically reduces content
  effects: the interference of prior and current assumptions that both humans and
  language models have been shown to suffer from. Finally, we explore
  bootstrapping LLaMA 13B from its own reasoning and find that LogicGuide is
  critical: by training only on certified self-generated reasoning, LLaMA can
  self-improve, avoiding learning from its own hallucinations.
paper_ai_summary: A guide tool called LogicGuide improves language model accuracy in logical reasoning tasks by constraining generation to valid statements, reducing content effects and enabling self-improvement.
paper_ai_keywords: guides state constraints incremental constraints LogicGuide logical reasoning PrOntoQA ProofWriter GPT-3 GPT-3.5 Turbo LLaMA self-improvement certified self-generated reasoning