Summary of Retrieval Instead Of Fine-tuning: a Retrieval-based Parameter Ensemble For Zero-shot Learning, by Pengfei Jin et al.
Retrieval Instead of Fine-tuning: A Retrieval-based Parameter Ensemble for Zero-shot Learning
by Pengfei Jin, Peng Shu, Sekeun Kim, Qing Xiao, Sifan Song, Cheng Chen, Tianming Liu, Xiang Li, Quanzheng Li
First submitted to arxiv on: 13 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This research paper introduces a novel method called Retrieval-based Parameter Ensemble (RPE) for adapting pre-trained foundation models to new tasks. RPE leverages Low-Rank Adaptation (LoRA) techniques, which have shown success in fine-tuning large models, and combines them with Retrieval-Augmented Generation (RAG) methods that utilize vectorized databases. The proposed approach creates a database of LoRAs, enabling efficient retrieval and application of model adaptations without requiring extensive training or labeled data. This makes RPE suitable for zero-shot learning and particularly effective in privacy-sensitive domains like healthcare, where it modifies model parameters without accessing raw data. Experimental results demonstrate the effectiveness of RPE in tasks such as medical report generation and image segmentation, surpassing supervised fine-tuning methods in certain cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to adapt powerful AI models for new tasks without needing lots of training or labeled data. The method is called Retrieval-based Parameter Ensemble (RPE) and it combines two existing techniques to make the process more efficient and private. RPE creates a special database that allows it to quickly find and apply useful adaptations to the model, making it ideal for situations where data is limited or sensitive. In experiments, RPE outperformed other methods in certain tasks like generating medical reports and segmenting images. |
Keywords
» Artificial intelligence » Fine tuning » Image segmentation » Lora » Low rank adaptation » Rag » Retrieval augmented generation » Supervised » Zero shot
