# Comparing Traditional and LLM-based Search for Image Geolocation

arXiv: https://arxiv.org/abs/2401.10184 · HTML: https://mlsanity.com/paper/2401.10184/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2024.

## Built on by (most cited first, 10 of 10)

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- [Source Coverage and Citation Bias in LLM-based vs. Traditional Search Engines](https://mlsanity.com/paper/2512.09483/) (2025)
- [Where on Earth? A Vision-Language Benchmark for Probing Model Geolocation Skills Across Scales](https://mlsanity.com/paper/2510.10880/) (2025)
- [Understanding Help-Seeking Behavior of Students Using LLMs vs. Web Search for Writing SQL Queries](https://mlsanity.com/paper/2408.08401/) (2024)
- [GCI-ViTAL: Gradual Confidence Improvement with Vision Transformers for Active Learning on Label Noise](https://mlsanity.com/paper/2411.05939/) (2024)
- [Toward Reliable VLM: A Fine-Grained Benchmark and Framework for Exposure, Bias, and Inference in Korean Street Views](https://mlsanity.com/paper/2506.03371/) (2025)
- [IMAIA: Interactive Maps AI Assistant for Travel Planning and Geo-Spatial Intelligence](https://mlsanity.com/paper/2507.06993/) (2025)
- [GeoAgent: Evaluating VLM Geolocalization Through Embodied Navigation](https://mlsanity.com/paper/2608.29483/) (2026)

## Abstract (arXiv)

Web search engines have long served as indispensable tools for information retrieval; user behavior and query formulation strategies have been well studied. The introduction of search engines powered by large language models (LLMs) suggested more conversational search and new types of query strategies. In this paper, we compare traditional and LLM-based search for the task of image geolocation, i.e., determining the location where an image was captured. Our work examines user interactions, with a particular focus on query formulation strategies. In our study, 60 participants were assigned either traditional or LLM-based search engines as assistants for geolocation. Participants using traditional search more accurately predicted the location of the image compared to those using the LLM-based search. Distinct strategies emerged between users depending on the type of assistant. Participants using the LLM-based search issued longer, more natural language queries, but had shorter search sessions. When reformulating their search queries, traditional search participants tended to add more terms to their initial queries, whereas participants using the LLM-based search consistently rephrased their initial queries.