Multi Hop Reasoning

Multi-hop reasoning focuses on enabling AI systems, particularly large language models (LLMs), to solve problems requiring multiple inferential steps by integrating information from various sources. Current research emphasizes improving the accuracy and robustness of these systems, addressing challenges like susceptibility to misleading information and the need for efficient reasoning pathways, often using techniques like chain-of-thought prompting, reinforcement learning, and knowledge graph integration. This area is crucial for advancing AI capabilities in complex tasks such as question answering, knowledge base completion, and decision-making, impacting both scientific understanding of reasoning and the development of more reliable and powerful AI applications.