Satellite technology has significantly influenced our daily lives, manifested in applications such as navigation and communication. With its development, a vast amount of multimodal satellite commonsense data has been generated, thus leading to an urgent demand for conversation about satellite data. However, existing large language models suffer from prevalent hallucinations and poor comprehensibility on multimodal satellite data due to their high professional content threshold and partial information opacity. To address these issues, we propose a multimodal satellite knowledge retrieval-augmented iterative alignment framework (Sat-RIA) for satellite commonsense conversation. We first construct multi-view retrieval expert knowledge to reduce hallucinations and enhance the interpretability of responses, which incorporates the satellite expert database, satellite rule, satellite image database, and a satellite knowledge graph. We next design commonsense conversation instructions to make the answers more legible and understandable. Furthermore, the retrieval-augmented iterative alignment module refines response precision by aligning outputs with task-specific standards through multi-stage evaluations. Finally, we construct satellite multi-turn dialogue and visual question-answer datasets for a more comprehensive evaluation of satellite commonsense conversation. Experimental results demonstrate that Sat-RIA outperforms existing large language models and provides more comprehensible answers with fewer hallucinations.

The proliferation of satellite-related commonsense data on the internet. Traditional analytical methods are challenging to integrate and effectively uncover implicit knowledge within them. However, current deep learning and LLM-based approaches often struggle with errors and hallucinations when performing multi-hop reasoning in domain-specific contexts. To address these limitations, we propose a novel multi-hop reasoning framework for implicit commonsense mining. This framework aims to uncover the underlying meta-knowledge behind reasoning problems, thereby providing enhanced interpretability of the reasoning process. Specifically, we design an extraction-retrieval-principle multi-step reasoning method that generates different levels of meta-knowledge in stages to support the reasoning process effectively. We further design the mixture of expert knowledge graph construction to construct a satellite knowledge graph that supports multi-hop reasoning. Experimental results demonstrate that our approach outperforms baselines on satellite knowledge graph reasoning.