Certified Deductive Reasoning with Language Models

• URL: http://arxiv.org/abs/2306.04031v2
• Date: Wed, 8 Nov 2023 01:53:31 GMT
• Title: Certified Deductive Reasoning with Language Models
• Authors: Gabriel Poesia, Kanishk Gandhi, Eric Zelikman, Noah D. Goodman
• Abstract summary: We introduce a class of tools for language models called emphguides, 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. We show how a general system for logical reasoning can be used as a guide, which we call textscLogicGuide.
• Score: 37.51289654360009
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Language models often achieve higher accuracy when reasoning step-by-step in complex tasks. However, even when arriving at a correct final answer, their rationales are often logically unsound or inconsistent. This is a major issue when reliable reasoning traces are needed, such when fine-tuning on model-generated reasoning for self-improvement. To tackle these issues, we introduce a class of tools for language models called \emph{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 \textsc{LogicGuide}. Given a reasoning problem in natural language, a model can formalize its assumptions for \textsc{LogicGuide} and guarantee that its step-by-step reasoning is sound. In experiments on PrOntoQA, ProofWriter and Syllogism Validity datasets, \textsc{LogicGuide} significantly improves the performance of GPT-3, GPT-3.5 Turbo and LLaMA (accuracy gains up to 35\%), while drastically reducing \emph{content effects} -- the interference between unwanted prior assumptions and reasoning, which humans and language models suffer from. We then explore bootstrapping GPT-3.5 Turbo and LLaMA using their own reasoning traces. We find that LogicGuide is critical: by training only on certified self-generated reasoning, models can self-improve, avoiding learning from their own hallucinations. Moreover, bootstrapped models enjoy significant boosts on ReClor, a challenging real-world reasoning dataset, even when not relying on formalization at inference time.

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