Image geolocation is a critical task in various image-understanding
applications. However, existing methods often fail when analyzing challenging,
in-the-wild images. Inspired by the exceptional background knowledge of
multimodal language models, we systematically evaluate their geolocation
capabilities using a novel image dataset and a comprehensive evaluation
framework. We first collect images from various countries via Google Street
View. Then, we conduct training-free and training-based evaluations on
closed-source and open-source multi-modal language models. we conduct both
training-free and training-based evaluations on closed-source and open-source
multimodal language models. Our findings indicate that closed-source models
demonstrate superior geolocation abilities, while open-source models can
achieve comparable performance through fine-tuning.

利用多模态语言模型，我们系统评估其图片地理定位能力，并通过新的图像数据集和全面的评估框架进行训练及非训练的评估。结果表明，闭源模型展示出更好的地理定位能力，而开源模型通过微调可达到相当的性能。

LLMGeo: 大规模语言模型在野外图像地理定位中的基准测试

LLMGeo: Benchmarking Large Language Models on Image Geolocation  In-the-wild

Instruction tuning is instrumental in enabling Large Language Models~(LLMs)
to follow user instructions to complete various open-domain tasks. The success
of instruction tuning depends on the availability of high-quality instruction
data. Owing to the exorbitant cost and substandard quality of human annotation,
recent works have been deeply engaged in the exploration of the utilization of
powerful closed-source models to generate instruction data automatically.
However, these methods carry potential risks arising from the usage
requirements of powerful closed-source models, which strictly forbid the
utilization of their outputs to develop machine learning models. To deal with
this problem, in this work, we explore alternative approaches to generate
high-quality instruction data that do not rely on closed-source models. Our
exploration includes an investigation of various existing instruction
generation methods, culminating in the integration of the most efficient
variant with two novel strategies to enhance the quality further. Evaluation
results from two benchmarks and the GPT-4 model demonstrate the effectiveness
of our generated instruction data, which can outperform Alpaca, a method
reliant on closed-source models. We hope that more progress can be achieved in
generating high-quality instruction data without using closed-source models.

探索不依赖于封闭源模型生成高质量指令数据的替代方法，并通过整合有效的变体和两种新策略进一步提高其质量，我们的生成指令数据能够胜过依赖封闭源模型的 Alpaca 方法。希望在不使用封闭源模型的情况下能够取得更多的高质量指令数据生成进展。