Reasoning with Language Model Prompting: A Survey (2023)

Abstract: Reasoning, as an essential ability for complex problem-solving, can provide back-end support for various real-world applications, such as medical diagnosis, negotiation, etc. This paper provides a comprehensive survey of cutting-edge research on reasoning with language model prompting. We introduce research works with comparisons and summaries and provide systematic resources to help beginners. We also discuss the potential reasons for emerging such reasoning abilities and highlight future research directions. Resources are available at https://github.com/zjunlp/Prompt4ReasoningPapers (updated periodically).

## introduction reasoning ability lies at the heart of human intelligence, yet in natural language processing (nlp), modern neural networks can hardly reason from what they are told or have already known @xcite @xcite . fortunately, with the revolutionary development of pre-training @xcite @xcite , scaling up the size of language models (lms) has shown to confer a range of reasoning abilities, such as arithmetic @xcite , commonsense @xcite , symbolic @xcite reasoning. as shown in figure 1 , such abilities may be unlocked by prompting strategies @xcite (e.g., chain-of-thought (cot) prompting @xcite , generated knowledge prompting @xcite ), which can dramatically narrow the gap between human and machine intelligence. likewise, a vast amount of work has been proposed in the nlp community; however, these approaches, scattered among various tasks, have not been systematically reviewed and analyzed. ## preliminaries in this section, we introduce preliminaries of reasoning with lm prompting. for standard prompting, given the reasoning question q, prompt t and parameterized probabilistic model p lm , we aim to maximize the likelihood of answer a as: ## taxonomy of methods in this paper, we survey existing reasoning methods with lm prompting, categorizing them as strategy enhanced reasoning ( §3.1) and knowledge enhanced reasoning ( §3.2). as shown in figure 2 , ## conclusion and vision in this paper, we provide a review of reasoning with language model prompting, including comprehensive comparisons, and several research directions. in the future, we envision a more potent synergy between the methodologies from the nlp and other domains and hope sophisticated and efficient lm prompting models will increasingly contribute to improving reasoning performance.