Logical reasoning is central to human cognition and intelligence. It includes deductive, inductive, and abductive reasoning. Past research of logical reasoning within AI uses formal language as knowledge representation and symbolic reasoners. However, reasoning with formal language has proved challenging (e.g., brittleness and knowledge-acquisition bottleneck). This paper provides a comprehensive overview on a new paradigm of logical reasoning, which uses natural language as knowledge representation and pretrained language models as reasoners, including philosophical definition and categorization of logical reasoning, advantages of the new paradigm, benchmarks and methods, challenges of the new paradigm, possible future directions, and relation to related NLP fields. This new paradigm is promising since it not only alleviates many challenges of formal representation but also has advantages over end-to-end neural methods. This survey focus on transformer-based LLMs explicitly working on deductive, inductive, and abductive reasoning over English representation.

翻译：逻辑推理是人类认知与智能的核心，包含演绎推理、归纳推理和溯因推理。早期人工智能领域的逻辑推理研究采用形式化语言作为知识表示，并依赖符号推理器。然而，形式化语言推理面临诸多挑战（如脆弱性和知识获取瓶颈）。本文系统综述了一种新型逻辑推理范式，该范式采用自然语言作为知识表示，并以预训练语言模型作为推理器，涵盖逻辑推理的哲学定义与分类、新范式的优势、基准测试与方法、面临的挑战、未来可能方向及其与自然语言处理相关领域的关系。这一新范式具有广阔前景，不仅缓解了形式化表示的多项难题，还相较于端到端神经网络方法展现出独特优势。本综述聚焦于基于Transformer的大语言模型，针对英语表示开展演绎、归纳与溯因推理的研究。