Certified Reasoning with Language Models
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.
64812cebc0d3c70316d54fce
Gabriel Poesia
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2023-06-09T15:32:56.108Z
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Gabriel Poesia
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gpoesia
64812cebc0d3c70316d54fcf
Kanishk Gandhi
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2023-06-09T15:32:52.230Z
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Kanishk Gandhi
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obiwan96
64812cebc0d3c70316d54fd0
Eric Zelikman
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2023-06-08T16:07:45.413Z
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/avatars/4cea5cbd4ee918f1de7ccf191bfdf118.svg
Eric Zelikman
user
ezelikman
64812cebc0d3c70316d54fd1
Noah D. Goodman
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2024-11-15T09:56:22.538Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/ltcQhre6eDRVzn6Vbbyhu.png
Noah D. Goodman
user
ngoodman
Certified Reasoning with Language Models
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.
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.
guides state constraints incremental constraints LogicGuide logical reasoning PrOntoQA ProofWriter GPT-3 GPT-3.5 Turbo LLaMA self-improvement certified self-generated reasoning