Summary of On the Utility Of Domain-adjacent Fine-tuned Model Ensembles For Few-shot Problems, by Md Ibrahim Ibne Alam et al.
On the Utility of Domain-Adjacent Fine-Tuned Model Ensembles for Few-shot Problems
by Md Ibrahim Ibne Alam, Parikshit Ram, Soham Dan, Horst Samulowitz, Koushik Kar
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 framework called DAFT-E combines an ensemble of domain-adjacent fine-tuned foundation models to tackle few-shot problems. The approach shows comparable accuracy to the best single model in zero-shot scenarios, and even surpasses individual models with minimal data for fine-tuning in few-shot settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a super smart language model that can do many things, but it needs some special training to be really good at specific tasks. This paper looks at how to use many of these “smart” models together to make something even better. It’s like having a team of experts working together to solve problems! The new approach works well even when there’s very little data available for fine-tuning. |
Keywords
» Artificial intelligence » Few shot » Fine tuning » Language model » Zero shot
