Entailment Tree

Entailment trees are structured representations of logical reasoning, used to explain how conclusions are derived from evidence, particularly in question-answering systems. Current research focuses on improving the generation of these trees using various methods, including iterative retrieval-generation models, reinforcement learning approaches, and the incorporation of hierarchical semantics and logical patterns within hyperbolic embedding spaces. This work aims to enhance the explainability and trustworthiness of AI systems by providing transparent and verifiable reasoning paths, impacting fields like question answering and multimodal fact verification.