Summary of An Ecosage Assistant: Towards Building a Multimodal Plant Care Dialogue Assistant, by Mohit Tomar et al.
An EcoSage Assistant: Towards Building A Multimodal Plant Care Dialogue Assistant
by Mohit Tomar, Abhisek Tiwari, Tulika Saha, Prince Jha, Sriparna Saha
First submitted to arxiv on: 10 Jan 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 The proposed work aims to build a plant care assistant that can assist individuals with plant-related concerns through conversations. The authors develop a dataset, Plantational, containing around 1K dialogues between users and plant care experts. They benchmark this dataset using various large language models (LLMs) and visual language models (VLMs), exploring the impact of instruction tuning and fine-tuning techniques on the task. Additionally, they propose EcoSage, a multi-modal plant care assisting dialogue generation framework that incorporates an adapter-based modality infusion using a gated mechanism. The performance of various LLMs and VLMs is evaluated through automated and manual evaluation to highlight their strengths and weaknesses in generating domain-specific dialogue responses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a special assistant for people who want to take good care of plants. They made a big collection of conversations between people who know about plants and those who don’t, called Plantational. Then, they tested different language models on this dataset to see how well they could understand what people are asking about their plants and give helpful responses. The goal is to make it easier for people to take care of plants by having a conversation with an expert-like AI system. |
Keywords
* Artificial intelligence * Fine tuning * Instruction tuning * Multi modal
