Summary of Cost-effective Instruction Learning For Pathology Vision and Language Analysis, by Kaitao Chen et al.
Cost-effective Instruction Learning for Pathology Vision and Language Analysis
by Kaitao Chen, Mianxin Liu, Fang Yan, Lei Ma, Xiaoming Shi, Lilong Wang, Xiaosong Wang, Lifeng Zhu, Zhe Wang, Mu Zhou, Shaoting Zhang
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 A novel approach to conversational pathology, called CLOVER, is proposed as a cost-effective instruction learning framework. This framework trains a lightweight module and uses instruction tuning on GPT-3.5, rather than relying on costly models like GPT-4. The goal is to build generation-based instructions that utilize pathological knowledge derived from the Internet. A high-quality set of template-based instructions was constructed in the context of digital pathology, and results show the strength of hybrid-form instructions in visual question-answer tasks. CLOVER outperforms strong baselines with 37 times more training parameters, demonstrating its potential to accelerate the adoption of rapid conversational applications in digital pathology. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CLOVER is a new way for computers and doctors to talk. Right now, it’s hard to train these chatbots because they need lots of data, money, and computer power. CLOVER solves this problem by using a smaller model that doesn’t require as much training data or resources. It also uses special prompts on a larger language model called GPT-3.5 to create instructions for the chatbot. These instructions are based on information from the Internet about diseases. The researchers tested CLOVER and found it worked well, even better than more powerful models that needed more training data. This could help make it easier for doctors to use computers to answer questions and diagnose diseases. |
Keywords
» Artificial intelligence » Gpt » Instruction tuning » Language model
