Mitigating Geospatial Knowledge Hallucination in Large Language Models: Benchmarking and Dynamic Factuality Aligning (2025)

Abstract: Large language models (LLMs) possess extensive world knowledge, including geospatial knowledge, which has been successfully applied to various geospatial tasks such as mobility prediction and social indicator prediction. However, LLMs often generate inaccurate geospatial knowledge, leading to geospatial hallucinations—incorrect or inconsistent representations of geospatial information—that compromise their reliability. While the phenomenon of general knowledge hallucination in LLMs has been widely studied, the systematic evaluation and mitigation of geospatial hallucinations remain largely unexplored. To address this gap, we propose a comprehensive evaluation framework for geospatial hallucinations, leveraging structured geospatial knowledge graphs for controlled assessment. Through extensive evaluation across 20 advanced LLMs, we uncover the hallucinations in their geospatial knowledge. Building on these insights, we introduce a dynamic factuality aligning method based on Kahneman-Tversky Optimization (KTO) to mitigate geospatial hallucinations in LLMs, leading to a performance improvement of over 29.6% on the proposed benchmark. Extensive experimental results demonstrate the effectiveness of our benchmark and learning algorithm in enhancing the trustworthiness of LLMs in geospatial knowledge and reasoning tasks.

## introduction recently, large language models (llms), known for their excellent reasoning abilities @xcite and extensive world knowledge @xcite , have been widely applied across various domains.the extensive geospatial knowledge embedded in llms has also been is the llm's response accurate when it comes to a real-world spatial knowledge? how can we improve llms' factuality effectively and efficiently? figure 1 : an overview of our work. in order to address real-world geospatial hallucination of llms. we propose 1) geohalubench to detect and evaluate geospatial knowledge errors, 2) dynamickto to enhance llms' factuality effectively and efficiently. while the world knowledge embedded in llms has contributed to their widespread success in various applications over the past two years, researchers have identified significant errors and selfcontradictions-referred to as hallucinations-in the generated results of llms, particularly in domain-specific areas @xcite . these hallucinations significantly affect the trustworthiness of llms and their performance in real-world applications. geospatial knowledge within llms also exhibits notable hallucinations in practical use cases @xcite . detecting and mitigating these hallucina-tions has become a critical problem for the development and reliable deployment of llms in the real world. while various solutions have been proposed for general and domain-specific knowledge hallucinations @xcite , systematic evaluation and mitigation of geospatial knowledge hallucinations remain largely unexplored. this is particularly challenging due to two main factors. first, geospatial data is complex and diverse, resulting in varied manifestations of associated spatial hallucinations. second, general hallucination reduction methods often fail to account for the unique characteristics of geospatial knowledge, e.g., various elements and relations between them. in this paper, we propose a systematic benchmark and an effective factuality aligning method to evaluate and mitigate geospatial hallucinations in llms. we first build spatialkg to reorganize the diverse and unstructured geospatial data, and introduce a set of geospatial evaluation questions, along with a systematic taxonomy of hallucinations. furthermore, the elements in the evaluation questions are flexible and customizable, allowing for a thorough localization of the shortcomings in geospatial hallucinations across various llms. after evaluating 20 advanced llms using our proposed benchmark, we find that most of them, especially the open-source llms, exhibit significant hallucinations. based on our observations of these geospatial hallucinations, we develop dynamickto to effectively mitigate the geospatial hallucinations in smaller-scale, open-source llms by enhancing the kto algorithm with dynamic factuality aligning. with the help of dynamickto, llama3.1-8b achieves a significant performance improvement on the proposed benchmark and demonstrates competitive performance compared to the second-best model among the 20 advanced llms we evaluated. in summary, our contributions are threefold. ## related work geospatial knowledge in llms trained on largescale text corpora, llms have acquired extensive world knowledge @xcite , including global geospatial information @xcite . this embedded geospatial knowledge has inspired the potential application of llms in various knowledge-intensive geospatial tasks, such as global geospatial prediction @xcite -including health, education, and poverty level estimation-mobility prediction @xcite ) using text-based addresses, and urban task planning @xcite . however, due to the limitations of online corpora in capturing real-world information, researchers have explored various fine-tuning methods to enhance llms' geospatial knowledge, such as citygpt @xcite and lamp @xcite . unlike these studies, which focus on leveraging llms' geospatial knowledge for specific tasks, our work is the first to systematically evaluate geospatial knowledge hallucinations and propose an effective mechanism to mitigate them. hallucination evaluation of llms. the hallucination problem in llms has been widely studied @xcite , with numerous evaluation benchmarks @xcite @xcite and training methods @xcite proposed to address general hallucination issues in llms. for hallucination evaluation and detection, niels et al. @xcite investigate the problem of self-contradiction, while manakul et al. @xcite introduce selfcheck-gpt, a simple sampling-based method to detect hallucinations. min et al. @xcite hallucination reduction of llms. to mitigate hallucinations in llms, one simple approach involves using retrieval-augmented generation (rag) methods with external knowledge bases during generation @xcite . however, rag-based methods are resource-intensive, requiring a large number of tokens and time during inference. as a result, researchers have continued to explore more efficient methods @xcite @xcite to effectively mitigate hallucinations in various domains. for instance, zhang @xcite proposes knowpat, which constructs a preference set and introduces a new alignment objective for service and urology. chen et al. @xcite propose halc, a robust auto-focal grounding mechanism for reducing object hallucinations in vision-language models (vlms), while tian et al. @xcite develop facttune, a fine-tuning method aimed at reducing hallucinations in biographies and medical queries. in contrast to these works focused on general knowledge and domain-specific hallucination evaluation and mitigation, our research specifically targets the evaluation and mitigation of geospatial knowledge hallucinations in llms. ## conclusion we propose a framework to systematically evaluate and mitigate geospatial hallucinations in llms. using a dedicated taxonomy and controllable evaluation design, we assess 20 advanced llms and provide a comprehensive analysis. to improve performance, we enhance the kto algorithm with dynamic factuality aligning, accounting for geospatial data diversity. with dynamickto, smaller opensource models achieve competitive results against top-performing llms in hallucination mitigation.