Papers by James Hays
Granular Privacy Control for Geolocation with Vision Language Models (2024.emnlp-main)
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| Challenge: | Vision Language Models (VLMs) are rapidly advancing in their capability to answer information-seeking questions. |
| Approach: | They develop a benchmark to evaluate the ability of VLMs to moderate geolocation dialogues with users. |
| Outcome: | a new benchmark evaluates the ability of VLMs to moderate geolocation conversations with users. |
GeoRC: A Benchmark for Geolocation Reasoning Chains (2026.acl-long)
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Mohit Talreja, Joshua Diao, Jim James, Radu Casapu, Tejas Santanam, Ethan Mendes, Alan Ritter, Wei Xu, James Hays
| Challenge: | Vision Language Models (VLMs) are good at recognizing the global location of a photograph but are startlingly bad at explaining which image evidence led to their location prediction. |
| Approach: | They propose a benchmark for geolocation reasoning chains based on the global location prediction task in the popular GeoGuessr game. |
| Outcome: | The proposed benchmark compares LLM-as-a-judge and VLM-As-jumble strategies against human scoring. |