Summary of Transgpt: Multi-modal Generative Pre-trained Transformer For Transportation, by Peng Wang et al.
TransGPT: Multi-modal Generative Pre-trained Transformer for Transportation
by Peng Wang, Xiang Wei, Fangxu Hu, Wenjuan Han
First submitted to arxiv on: 11 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 TransGPT is a novel large language model designed specifically for the transportation domain. The model consists of two variants: TransGPT-SM, finetuned on single-modal data from various sources, and TransGPT-MM, trained on multi-modal data from three areas of the transportation domain (driving tests, traffic signs, and landmarks). These models outperform baseline models on most tasks in benchmark datasets for different transportation-related applications. The potential uses of TransGPT include generating synthetic traffic scenarios, explaining traffic phenomena, answering traffic-related questions, providing traffic recommendations, and generating traffic reports. This work advances the state-of-the-art of natural language processing (NLP) in the transportation domain and provides a useful tool for ITS researchers and practitioners. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TransGPT is a new way to use computers to understand and work with information about transportation, like roads and traffic. It’s special because it can handle different types of data, like words, images, and videos, all at once. The model was tested on lots of examples and did better than other models in most cases. This could be useful for things like predicting traffic patterns, explaining why there are traffic jams, answering questions about traffic, giving advice on how to get around, and writing reports about traffic. |
Keywords
» Artificial intelligence » Large language model » Multi modal » Natural language processing » Nlp
