Summary of Agrogpt: Efficient Agricultural Vision-language Model with Expert Tuning, by Muhammad Awais et al.
AgroGPT: Efficient Agricultural Vision-Language Model with Expert Tuning
by Muhammad Awais, Ali Husain Salem Abdulla Alharthi, Amandeep Kumar, Hisham Cholakkal, Rao Muhammad Anwer
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 The proposed approach constructs instruction-tuning data for the agriculture domain using vision-only data, leveraging diverse agricultural datasets and large language models. The resulting 70k expert-tuning dataset, AgroInstruct, is used to create an efficient large multimodal conversational model (LMM) called AgroGPT. This model excels at identifying fine-grained agricultural concepts, can act as an agriculture expert, and provides helpful information for multimodal agriculture questions. The performance of AgroGPT is evaluated using AgroEvals and compared to large open and closed-source models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help computers understand complex conversations about farming has been developed. Currently, computers struggle to talk about new topics like agriculture without having lots of information about that topic beforehand. To fix this, a special dataset was created by combining many images and texts related to farming. This dataset, called AgroInstruct, helped train a computer model called AgroGPT to understand and respond to complex questions about farming. AgroGPT can identify important details in farming, act like an expert farmer, and provide helpful answers. The new approach is important because it helps computers talk about new topics without needing a lot of information beforehand. |
Keywords
» Artificial intelligence » Instruction tuning
