Summary of Visual Editing with Llm-based Tool Chaining: An Efficient Distillation Approach For Real-time Applications, by Oren Sultan et al.
Visual Editing with LLM-based Tool Chaining: An Efficient Distillation Approach for Real-Time Applications
by Oren Sultan, Alex Khasin, Guy Shiran, Asnat Greenstein-Messica, Dafna Shahaf
First submitted to arxiv on: 3 Oct 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 Our paper presents a novel approach to fine-tune large language models (LLMs) for real-time applications involving visual editing tasks. Specifically, we focus on modifying images and videos based on user stylistic requests specified in natural language. We leverage LLMs like GPT-3.5-Turbo but find their high cost and latency unsuitable for real-time use cases. Instead, we propose fine-tuning a smaller student LLM with guidance from a larger teacher LLM and behavioral signals. Our offline metrics evaluate the performance of these student models, which match that of our teacher model (GPT-3.5-Turbo) while reducing costs and latency by up to 25%. We also demonstrate improved fine-tuning results in low-data regimes using data augmentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’re working on a way to use big language models for editing pictures and videos. Right now, these models are too expensive and slow to be used for real-time applications. Our idea is to train a smaller model that can learn from a bigger, more powerful model. We tested this approach and found that it works just as well as the original, but much faster and cheaper! We also showed that this method works even better when we add some extra information to help it learn. |
Keywords
» Artificial intelligence » Data augmentation » Fine tuning » Gpt » Teacher model
