Summary of Evaluating Tool-augmented Agents in Remote Sensing Platforms, by Simranjit Singh et al.
Evaluating Tool-Augmented Agents in Remote Sensing Platforms
by Simranjit Singh, Michael Fore, Dimitrios Stamoulis
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper presents GeoLLM-QA, a benchmark designed to evaluate large language models (LLMs) in remote sensing (RS) applications. The existing benchmarks assume predefined image-text data pairs and question-answering input templates, which neglect the complexities of realistic user-grounded tasks. To address this gap, GeoLLM-QA captures long sequences of verbal, visual, and click-based actions on a real UI platform, enabling the evaluation of state-of-the-art LLMs over a diverse set of 1,000 tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper creates a new way to test big language models in remote sensing. Right now, tests assume that users give specific questions and images, which isn’t how people really work. For example, if someone is looking at a map, they might zoom in on an area, draw a boundary around it, and then ask “What’s going on here?” But these tests don’t account for the way people use systems, like clicking on things or giving verbal commands. To fix this, the researchers created a new test that captures how users interact with systems, including verbal, visual, and click-based actions. They tested big language models on 1,000 different tasks to see which ones work best. |
Keywords
» Artificial intelligence » Question answering
