Summary of Directed Domain Fine-tuning: Tailoring Separate Modalities For Specific Training Tasks, by Daniel Wen and Nafisa Hussain
Directed Domain Fine-Tuning: Tailoring Separate Modalities for Specific Training Tasks
by Daniel Wen, Nafisa Hussain
First submitted to arxiv on: 24 Jun 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 This research paper explores the application of large language models (LLMs) and visual language models (LVLMs) for specific tasks within distinct domains. The authors propose an approach to fine-tune model parameters using LORA, eliminating noise irrelevant to the task while enhancing precision. They demonstrate this approach on Video-LLaVA, a multimodal architecture that integrates image, video, and text encoders. By providing cooking images and videos along with general cooking questions, they aim to remove noise unrelated to cooking and improve ingredient list and instruction generation capabilities. The authors achieve gains of 2% over the baseline Video-LLaVA on the YouCook2 dataset using an orders-of-magnitude smaller image instruction dataset. This work has implications for task-specific fine-tuning of LLMs and LVLMs, enabling more precise and relevant output. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is about improving machines that can understand and generate text or images. These machines, called language models, are great at generating general information but struggle when given specific tasks. The authors came up with a new way to teach these machines by giving them only the most relevant information for each task. They tested this approach on a machine that generates recipes from cooking videos without transcripts. By providing the machine with images and videos of cooking, along with some basic questions about cooking, they were able to improve the machine’s ability to generate accurate ingredient lists and instructions. This new way of teaching machines could lead to more accurate and helpful results in many areas. |
Keywords
» Artificial intelligence » Fine tuning » Lora » Precision
