Summary of Mafin: Enhancing Black-box Embeddings with Model Augmented Fine-tuning, by Mingtian Zhang et al.
Mafin: Enhancing Black-Box Embeddings with Model Augmented Fine-Tuning
by Mingtian Zhang, Shawn Lan, Peter Hayes, David Barber
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 In this paper, researchers introduce Model Augmented Fine-tuning (Mafin), a novel approach for fine-tuning black-box embedding models in Retrieval Augmented Generation (RAG) systems. The Mafin method enhances the performance of pre-trained embeddings by augmenting them with trainable embedding models, without requiring extensive retraining. This innovation is particularly effective when dealing with domain-specific knowledge and improves the overall retrieval accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a problem in large language models. It shows how to make better predictions from what we already know about language, so that computers can understand us more accurately. The solution involves using new ideas to improve old ways of doing things, making it easier for computers to find the right answer. |
Keywords
* Artificial intelligence * Embedding * Fine tuning * Rag * Retrieval augmented generation
